Human Readers Research Articles

Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

To study the feasibility of a channelized Hotelling observer (CHO) to predict human observer performance in detecting calcification-like signals in mammography images of an anthropomorphic breast phantom, as part of a quality control (QC) framework. A prototype anthropomorphic breast phantom with inserted gold disks of 0.25 mm diameter was imaged with two different digital mammography x-ray systems at four different dose levels. Regions of interest (ROIs) were extracted from the acquired processed and unprocessed images, signal-present and signal-absent. The ROIs were evaluated by a CHO using four different formulations of the difference of Gaussian (DoG) channel sets. Three human observers scored the ROIs in a two-alternative forced-choice experiment. We compared the human and the CHO performance on the simple task to detect calcification-like disks in ROIs with and without postprocessing. The proportion of correct responses of the human reader (PCH ) and the CHO (PCCHO ) was calculated and the correlation between the two was analyzed using a mixed-effect regression model. To address the signal location uncertainty, the impact of shifting the DoG channel sets in all directions up to two pixels was evaluated. Correlation results including the goodness of fit (r2 ) of PCH and PCCHO for all different parameters were evaluated. Subanalysis by system yielded strong correlations between PCH and PCCHO , with r2 between PCH and PCCHO was found to be between 0.926 and 0.958 for the unshifted and between 0.759 and 0.938 for the shifted channel sets, respectively. However, the linear fit suggested a slight system dependence. PCCHO with shifted channel sets increased CHO performance but the correlation with humans was decreased. These correlations were not considerably affected by of the DoG channel set used. There is potential for the CHO to be used in QC for the evaluation of detectability of calcification-like signals. The CHO can predict the PC of humans in images of calcification-like signals of two different systems. However, a global model to be used for all systems requires further investigation.

The semantics and pragmatics of quotation ed. by Paul Saka, Michael Johnson

Reviewed by: The semantics and pragmatics of quotation ed. by Paul Saka, Michael Johnson Erik Stei The semantics and pragmatics of quotation. Ed. by Paul Saka and Michael Johnson. Dordrecht: Springer, 2017. Pp. viii, 382. ISBN 9783319687469. $149.99. The semantics and pragmatics of quotation, edited by Paul Saka and Michael Johnson, is a collection of papers on quotation and quotation-like constructions that forms part of the ‘Perspectives in pragmatics, philosophy & psychology’ series edited by Alessandro Capone. Most papers in the volume offer state-of-the-art research on current debates in the literature on quotation, so the collection will appeal mostly to researchers familiar with the topic. The contributions are organized into three parts (I: ‘Use & mention’, II: ‘Quotation unified’, and III: ‘New directions’), and the papers generally fit those broad categories quite well. In this review, I give only short summaries of the papers in Parts I and III, but I have a bit more to say about the contributions in Part II. [End Page 383] In his ‘Scare-quoting and incorporation’, Mark McCullagh argues that some cases of scare quotes are best explained by a presemantic mechanism called ‘incorporation’. The basic idea is that when interpreting a sentence, we are sometimes required to switch the lexicon as well as the context midway through the sentence. McCullagh proposes a modification of Kaplan’s (1989) logic of demonstratives that allows for (i) complex contexts, consisting of n-tuples of ordinary Kaplanian contexts, and (ii) different sets of character-specifying lexical axioms. It is not entirely clear why this paper was included in Part I as it does not concern the use-mention distinction. Saka’s contribution is concerned with the development of a general account of ‘quasi-quotation’ or ‘unquotation’. The phenomenon occurs when an expression inside quotation marks is to be interpreted as not strictly quoted. Typically, but not necessarily, quasi-quotations are marked by devices like squared brackets in academic writing indicating additions or omissions by the reporter of the utterance. Saka delineates quasi-quotation in formal languages and metalanguages. He then identifies and characterizes quasi-quotational devices in natural language and specifies ten rules of quasi-quotation that cover ordinary quotation as a special case. Marga Reimer focuses on Saul Kripke’s (1980) remarks concerning the confusion of use and mention in ordinary discourse involving proper names and defends them against a number of criticisms. The paper makes some exegetical points, but it also offers an interesting discussion of constructions like ‘The F is called N’ and provides arguments for the view that those have the same meaning as their ‘The F is called “N”’ counterparts. Shomir Wilson reviews basic techniques of natural language processing and shows how those techniques reach their limits when applied to mentioned language. Wilson’s proposed remedy is a rubric for mentioned language that is to be thought of as an operationalization of human readers’ capacity to ‘intuit the presence of mentioned language in a sentence, even in the absence of stylistic cues’ (88). Part II, ‘Quotation unified’, starts with Kirk Ludwig and Greg Ray’s ‘Unity in the variety of quotation’. The aim of the paper is to identify a common core of quotational constructions. The idea is that quotation marks are polysemous and that the unifying feature of all uses of quotation marks involving reference to expressions is given by the disquotational rule (Q): for any φ, ‘φ’ refers to φ. Ludwig and Ray argue that direct quotation, mixed quotation, and what they call ‘uses of quotation marks in scholarly exposition’ all involve reference to expressions and, thus, exhibit characteristic features of pure quotation. My only complaint here is that the title of the paper promises too much, as scare quotes, emphatic quotes, and other ‘varieties of quotation’ are set aside from the get-go. The main reason for this decision seems to be that in those uses ‘the word itself in quotation marks is not an object about which the sentence says anything, and is not used in determining whether it is true or false’ (104, n. 5). But this looks like another...

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

To gain a comprehensive overview of new scientific findings with the enormous, ever-increasing amount of published information, we apply a new combinatorial approach that complements the process of reading scientific articles by supplementing artificial intelligence technologies. We present a combinatorial approach, which we illustrate in the form of a “double funnel of artificial intelligence.” Our approach suggests to largely increase the amount of data at the beginning of the data collection process and to subsequently clean and enrich the data set in order to gain much more knowledge at the end of the procedure compared to a “classical” literature review. We use natural language processing and text visualization techniques to uncover findings that are generally unbeknown to the human reader due to the inability to process very large amounts of text. By illustrating the individual steps using practical examples taken from use cases, we demonstrate the merits of our approach. With our methodology, we are able to reproduce findings from “regular” review papers; however, we discover additional and new findings in different fields, such as data science or medicine. We also point out the limitations of our approach. Finally, we make suggestions as to how the methodology could be further developed.

Fish Is People

This article is based on the keynote address Perry Nodelman presented at the IBBY International Congress in Athens in August 2018. Most picturebooks about fish turn them into surrogates for human readers. After considering why and how they do so, Nodelman explores how seeing fish as people might affect young readers’ understanding of fish as fish.

Reboot Everything

Reboot Everything Aaron Jaffe (bio) Blockchain: The Next Everything Stephen P. Williams Scribner www.simonandschuster.com 208 Pages; Cloth, $24.95 Correction: The fifteenth page of this issue has been edited for typographical errors in the first paragraph. The editors and publishers apologize for the error. Whether or not blockchain is in fact "the next everything" is probably best left to our future robotic overlords. Stephen P. Williams's Blockchain: The Next Everything, the book attached to this philosophically vacuous title, is—to this human reader, at least—a dreary casserole of logical fallacies, outlandish claims, and unsummarizable discourse. Equal parts techno-solutionist propaganda piece, indoctrination gospel for the next big tech revolution, and self-help pamphlet for data survival after the extinction event, Blockchain: The Next Everything comes leveraged in an untestable future tense, loaded with aspirational bullet-pointery, modal verbs (could, should, would, might), and abundant dead space padding out every third page. It reads like The Onion sending-up a tech influencer drunk on memes, Crypto, and cyberlibertarianism, served with a chaser of Death-by-PowerPoint. The ideology of cyberlibertarianism, as David Golumbia puts it in The Politics of Bitcoin: Software as Right-Wing Extremism (2016), can be thought of as something like a belief according to which freedom will emerge inherently from the increasing development of digital technology, and therefore entails that efforts to interfere with or regulate that development must be antithetical to freedom—although what 'freedom' means in this context is much less clear than it may seem. It resonates with the so-called California ideology: "the presumption that computer-based expertise trumps that of all other forms of expertise, sometimes because everything in the world is ultimately reducible to computational processes." Relatedly, blockchain propaganda mobilizes a rich vein of deeply held tropes of extreme right-wing paranoia about money, markets, pricing, and value: that central banking such as that practiced by the U.S. Federal Reserve is a deliberate plot to "steal value" from the people to whom it actually belongs; that the world monetary system is on the verge of imminent collapse due to central banking policies, especially fractional reserve banking; that "hard" currencies such as gold provide meaningful protection against that purported collapse; that inflation is a plot to steal money from the masses and hand it over to a shadowy cabal of "elites" who operate behind the scenes; and more generally that the governmental and corporate leaders and wealthy individuals we all know are "controlled" by those same "elites." Make no mistake: Blockchain: The Next Everything is a brochure for something huge that its author can never quite get a hold of but nevertheless badly desires to peddle. Know your merchandise. If there only were something to sell—some miracle widget, a particular panacea, a branded patent medicine, a pill-pack of magic beans—it would be easier going on all of us. No mere huckster of cryptocurrency, Williams proceeds with all the glib solemnity of a seasoned TED talker to demand a hard system reboot of everything—i.e., modern society—around the underlying technical specs first developed for bitcoin. I can recommend two books on Blockchain that do more than fluffery: David Golumbia's Politics of Bitcoin: Software as Right-Wing Extremism and David Gerard's Attack of the 50 Foot Blockchain (2017). Bitcoin and Blockchain are different animals. Bitcoin is crypto, the front-end; blockchain is the backend, the massively decentralized, distributed database. Crypto runs on blockchain but so might other things: deeds and contracts, branded goods, markets for authentic antiques and Harry Potter first editions, exchanges for fairly sourced, artisanal rare-earths, the next eBay, the next Facebook, the next Amazon, the next Internet. A ginormous, uncrackable transaction ledger of automatically protected digital information, secured by difficult, electricity-sucking computational problems, blockchain is as blockchain does, distributing itself amongst its constituent participants, secured by their consent to transact upon the ledger, accessible to them separately only with complexly encrypted passkeys. The conceptual origins of both crypto and blockchain are suitably shrouded in mystery. In a mysterious white paper from 2008, on or about the banking crisis of that same year, the enigmatic...

Automatic Scribe Attribution for Medieval Manuscripts

We propose an automatic method for attributing manuscript pages to scribes. The system uses digital images as published by libraries. The attribution process involves extracting from each query page approximately letter-size components. This is done by means of binarization (ink-background separation), connected component labelling, and further segmentation, guided by the estimated typical stroke width. Components are extracted in the same way from the pages of known scribal origin. This allows us to assign a scribe to each query component by means of nearest-neighbour classification. Distance (dissimilarity) between components is modelled by simple features capturing the distribution of ink in the bounding box defined by the component, together with Euclidean distance. The set of component-level scribe attributions, which typically includes hundreds of components for a page, is then used to predict the page scribe by means of a voting procedure. The scribe who receives the largest number of votes from the 120 strongest component attributions is proposed as its scribe. The scribe attribution process allows the argument behind an attribution to be visualized for a human reader. The writing components of the query page are exhibited along with the matching components of the known pages. This report is thus open to inspection and analysis using the methods and intuitions of traditional palaeography. The present system was evaluated on a data set covering 46 medieval scribes, writing in Carolingian minuscule, Bastarda, and a few other scripts. The system achieved a mean top-1 accuracy of 98.3% as regards the first scribe proposed for each page, when the labelled data comprised one randomly selected page from each scribe and nine unseen pages for each scribe were to be attributed in the validation procedure. The experiment was repeated 50 times to even out random variation effects.

Semantic enrichment of life sciences content: how it works and key benefits for researchers.

In the time it takes a human life sciences researcher to read one research article machines can process hundreds of thousands of articles. An unco-ordinated army of bots, crawlers, and other software agents are active day and night on the Internet discovering, ingesting, and analyzing research content. Many of these agents are designed to help researchers rapidly filter the ever-expanding research record and surface the articles and findings most relevant to their work. For these software agents to be most effective, they need to understand the content they are reading in a manner similar to an expert human reader. (What are the main concepts being discussed and what are the main findings asserted? What is this research article telling us that is new and what is supporting or contradicting past findings?). This is where semantic enrichment comes into play - semantic enrichment adds structured machine-readable metadata to life science articles to assist software agents in 'reading' the content in a manner similar to a human researcher. In the present study, I'll define the mechanism of semantic enrichment of life sciences content, examine the benefits it is bringing to researchers today, and preview promising avenues for future benefits.

A postdigital paradigm in literary studies

ABSTRACTThe term postdigital has in recent years been applied across a broad range of disciplines, including literary studies, to describe an era in which digital media and technologies have become the dominant, if not hegemonic, aesthetic, social, epistemological and ontological paradigm. However, the full effects on literary studies of the new modes of literary production and consumption, the nascent reading practices and literary interfaces, and the inscrutable knowledge infrastructures that have emerged in this postdigital era remain unexamined.This article articulates a possible framework for understanding the literary object in this postdigital era, and in the twenty-first-century classroom. It extends scholarship in critical posthumanism and the digital humanities into the field of literary studies to describe how the human reader is entangled in complex, mutable networks of socio-material and technical relations that foreground the reading experience. Unpacking the current print-based assumptions of literary studies, it considers how the discipline might broaden and deepen its own theoretical, methodological and pedagogical approaches to the postdigital literary object. Finally, turning toward a critique of the signature pedagogy of literary studies, close reading, it speculates on the impact of these developments on the present and future of postdigital literary pedagogy.

Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests.

Routine quarterly quality assurance (QA) assessment of single photon emission computed tomography (SPECT) systems includes analysis of multipurpose phantoms containing spheres and rods of various sizes. When evaluated by accreditation agencies, criteria applied to assess image quality are largely subjective. Determining a quantified image characteristic metric that emulates human reader impressions of image quality could be quite useful. Our investigation was conducted to ascertain whether image texture analysis metrics, such as those applied to PET scans to detect neoplasms, could prove helpful in linking qualitative statements of phantom sphere and rod visibility to quantified parameters. Because it is not obvious whether it is preferable to submit reconstructions to accrediting agencies performed using typical clinical (CLIN) protocol processing parameters or to follow agencies' filtered backprojection (FBP) suggestions, we applied texture analysis metrics to determine the degree to which these choices affect equipment capability assessment. Data were processed retrospectively for 125 different Tc-99m SPECT scans of standardized phantoms for 14 rotating Anger detector systems as part of routine quarterly QA. Algorithms were written to compute several classes of image metrics: quantile curve metrics, image texture analysis gray-level co-occurrence matrix (GLCM) metrics, contrast metrics, and count histogram metrics. For qualitative image scores, two experienced physicists independently graded sphere and rod visibility on a 5-level scale and assigned dichotomous visibility scores, without knowledge of quantified texture analysis metrics or each other's readings. The same phantom was used to collect 15 additional data sets with two dual-detector SPECT/CT systems, reconstructed both by FBP parameters that have been suggested by accrediting agencies and by manufacturers' default settings for CLIN SPECT/CT bone imaging protocols by ordered subsets expectation maximization (OSEM), incorporating attenuation correction using the CT scan. Image characteristics metrics were compared for FBP and CLIN reconstructions. For spheres, the metric with the strongest rank correlation with 5-level scale readings was the quantile curve slope (ρ=0.83, P<0.0001), while for rods it was GLCM Energy normalized to the maximum GLCM Energy value (EnergyNorm) (ρ=-0.88, P<0.0001). Compared to dichotomous readings, the metric with the highest ROC area under curve (AUC) for spheres was the quantile curve slopes (AUC=96±1%, sensitivity=91%, specificity=90%), and for rods was EnergyNorm (AUC=98±1%, sensitivity=92%, specificity=95%). Image contrast was higher for all sphere sizes and rod EnergyNorm was lower for sectors of intermediate-sized rods for FBP compared to CLIN reconstructions, in agreement with more rods judged to be visible from FBP than CLIN reconstructions (47% vs 33%, P=0.002). When preparing to submit quality assurance images of standardized phantoms to accrediting agencies, a reliable gauge of sphere and rod visibility can be predicted accurately using quantified reader-independent image texture analysis metrics, which also provide a useful basis for choosing among alternative image reconstruction options.

CT automated exposure control using a generalized detectability index

Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index ( ) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches. This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index ( ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized metric is calculated using lookup tables of task-based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120kV and tube current varied manually from 20 to 240mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm with different regularization strengths (IR1-IR4). The task-based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between and noise standard deviation. The performance of the proposed -AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images. The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the predicted by the lookup table and measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the -AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the -AEC method, the observers' IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P>0.35). The -AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans. A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed -AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.

Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative.

We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging(MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data from the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets from the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers is achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We make the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.

Prospective Pilot Evaluation of Radiologists and Computer-aided Pulmonary Nodule Detection on Ultra-low-Dose CT With Tin Filtration.

The aim of this study was to evaluate the ability of computer-aided detection (CAD) and human readers to detect pulmonary nodules ≥5 mm using 100 kV ultra-low-dose computed tomography (ULDCT) utilizing a tin filter. After informed consent, 55 patients prospectively underwent standard-dose chest CT (SDCT) using 120 kV followed by ULDCT using 100 kV/tin. Reference nodules ≥5 mm were identified by a thoracic radiologist using SDCT. Four thoracic radiologists marked detected nodules on SDCT and ULDCT examinations using a dedicated computer workstation. After a 6-month memory extinction, readers were shown the same ULDCT cases with all CAD markings as well as their original detections, and characterized CAD detections as true positive or false positive. Volume CT Dose index (CTDIvol) for SDCT and ULDCT were 5.3±2 and 0.4±0.2 mGy (P<0.0001), respectively. Forty-five reference nodules were detected in 30 patients. Reader sensitivity varied widely but similarly for SDCT (ranging from 45% to 87%) and ULDCT (45% to 83%). CAD sensitivity was 76% (34/45) for SDCT and 71% (32/45) for ULDCT. After CAD, reader sensitivity substantially improved by 19% and 18% for 2 readers, and remained nearly unchanged for the other 2 readers (0% and 2%), despite reader perception that many more nodules were identified with CAD. There was a mean of 2 false-positive CAD detections/case. ULDCT with 100 kV/tin reduced patient dose by over 90% without compromising pulmonary nodule detection sensitivity. CAD can substantially improve nodule detection sensitivity at ULDCT for some readers, maintaining interobserver performance.

Program Text, Programming Style, Programmer Labor

Computer programming can be understood as both a kind of writing and a special class of human labor activity facilitating the automation of other human labor activities, not excluding itself. From the very beginning of the history of computer programming, it was recognized that the technical logic of programming is a logic of automation, indeed, a logic of self-automation or even recursive automation. This article suggests that a syntactic feature common to all programming languages used today serves as a register of this logic of automation, leaving traces in code that are legible if one knows where to look. That feature is the comment: natural-language text within a computer program that is written exclusively for human readers of the program and is separated from executable code, though it stands alongside it. At their most interesting, in patterns of use to which a managerial discourse of software craftspersonship is actively hostile, yet to which so-called literate programming practice is indifferent, program comments are traces of a mode of technical labor whose privilege and precarity both rest on its obscurity, an obscurity in no way resistant to reading.

Determination of mammographic breast density using a deep convolutional neural network.

High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas. In this study, 20,578 mammography single views from 5221 different patients (58.3 ± 11.5 years) were downloaded from the picture archiving and communications system of our institution and automatically sorted according to the ACR density (a-d) provided by the corresponding radiological reports. A dCNN with 11 convolutional layers and 3 fully connected layers was trained and validated on an augmented dataset. The model was finally tested on two different datasets against: i) the radiological reports and ii) the consensus decision of two human readers. None of the test datasets was part of the dataset used for the training and validation of the algorithm. The optimal number of epochs was 91 for medio-lateral oblique (MLO) projections and 94 for cranio-caudal projections (CC), respectively. Accuracy for MLO projections obtained on the validation dataset was 90.9% (CC: 90.1%). Tested on the first test dataset of mammographies (850 MLO and 880 CC), the algorithm showed an accordance with the corresponding radiological reports of 71.7% for MLO and of 71.0% for CC. The agreement with the radiological reports improved in the differentiation between dense and fatty breast for both projections (MLO = 88.6% and CC = 89.9%). In the second test dataset of 200 mammographies, a good accordance was found between the consensus decision of the two readers on both, the MLO-model (92.2%) and the right craniocaudal-model (87.4%). In the differentiation between fatty (ACR A/B) and dense breasts (ACR C/D), the agreement reached 99% for the MLO and 96% for the CC projections, respectively. The dCNN allows for accurate classification of breast density based on the ACR BI-RADS system. The proposed technique may allow accurate, standardized, and observer independent breast density evaluation of mammographies. Standardized classification of mammographies by a dCNN could lead to a reduction of falsely classified breast densities, thereby allowing for a more accurate breast cancer risk assessment for the individual patient and a more reliable decision, whether additional ultrasound is recommended.

Sound in silence: design and listening cultures in the Woodberry Poetry Room

This article examines the ways in which architecture and design forged a place for sound reproduction technologies inside the silent sanctuary of the modern library. During the first half of the twentieth century, recorded theatrical plays, interviews and readings entered the collections of public and private libraries, forcing universities to reckon with acoustic modalities of knowledge production and circulation. Focussing on the Woodberry Poetry Room, a small room designed by Aino and Alvar Aalto inside Harvard University’s Lamont Library, the article discusses how the two architects and designers addressed the acoustic challenge that record players presented to spaces of learning.Although an oddity in Aino and Alvar Aalto’s oeuvre, the Poetry Room holds a central position in the history of acoustic modernisms in architecture. Inside the room the two architects articulated their modernist critique of functionalism and technology, reclaiming listening spaces as the subject matter of design. In transforming each record player into a listening station in the middle of the room, their design turned the gramophone into a pillar of modern poetry. This interior marked a new sonic sensibility in architectural practices and discourses, indicative of the place that sound reproduction technologies, such as gramophones and record players, held in modern architecture as iconic objects of modernity and mass culture. The interior was also the meeting point for a new generation of poets, who gathered around those record players, plugged in, and listened together, yet alone, to the voices of modern poetries. In the 1960s, these same poets took over the New York scene, forcefully transforming the live performance of poetry. The article argues that architecture ultimately shaped the social space of reading and listening to poetry, and thus played an important role in producing new encounters between objects and human subjects, readers and sound reproduction technologies.

Improvement of prostate cancer detection combining a computer-aided diagnostic system with TRUS-MRI targeted biopsy.

To validate a novel consensus method, called target-in-target, combining human analysis of mpMRI with automated CAD system analysis, with the aim to increasing the prostate cancer detection rate of targeted biopsies. A cohort of 420 patients was enrolled and 253 patients were rolled out, due to exclusion criteria. 167 patients, underwent diagnostic 3T MpMRI. Two expert radiologists evaluated the exams adopting PI-RADSv2 and CAD system. When a CAD target overlapped with a radiologic one, we performed the biopsy in the overlapping area which we defined as target-in-target. Targeted TRUS-MRI fusion biopsy was performed in 63 patients with a total of 212 targets. The MRI data of all targets were quantitatively analyzed, and diagnostic findings were compared to pathologist's biopsy reports. CAD system diagnostic performance exhibited sensitivity and specificity scores of 55.2% and 74.1% [AUC = 0.63 (0.54 ÷ 0.71)] , respectively. Human readers achieved an AUC value, in ROC analysis, of 0.71 (0.63 ÷ 0.79). The target-in-target method provided a detection rate per targeted biopsy core of 81.8 % vs. a detection rate per targeted biopsy core of 68.6 % for pure PI-RADS based on target definitions. The higher per-core detection rate of the target-in-target approach was achieved irrespective of the presence of technical flaws and artifacts. A novel consensus method combining human reader evaluation with automated CAD system analysis of mpMRI to define prostate biopsy targets was shown to improve the detection rate per biopsy core of TRUS-MRI fusion biopsies. Results suggest that the combination of CAD system analysis and human reader evaluation is a winning strategy to improve targeted biopsy efficiency.

The “default” in our stars: Signposting non-defaultness in ironic discourse

ABSTRACTA non-default interpretation is required whenever speakers creatively depart from established norms and defaults. But effective speakers do not travel alone when they move away from default meanings to novel, non-default destinations. Effective speakers bring their readers with them, sometimes by making the non-default destination the only meaningful destination that can be reached with an utterance, but other times by helpfully—if subtly—marking their utterances to facilitate the dislocation of words and meanings. We consider the relative utility of different indicators of non-defaultness in this article, ranging from the subtle to the overt. Our approach argues for the usefulness of machine-generated texts when quantitatively exploring aspects of human linguistic creativity, since machine-tooled utterances can better assure the consistency and comparability of novel utterances that are designed to offer strikingly original points of view. Within this mechanical framework, we measure the extent of the shift from default to non-default interpretations via the downshift from positive to negative affect in machine-generated ironic utterances. In using machine-generated texts, our approach and its results also argue for the possibility that intelligent machines can craft creative utterances of their own, and effectively communicate an ironic point-of-view to human readers.

Computed tomography in axial spondyloarthritis.

Computed tomography (CT) is increasingly being used in ankylosing spondylitis (AS) for imaging the spine and sacroiliac joint (SIJ). We review new insights to diagnosis and evaluation revealed by the use of CT. Studies using low-dose CT in AS to detect syndesmophytes can image the entire spine, but semiquantitative scoring of the scans by human readers decreases the reliability and validity of this method. The thoracic spine is the segment most involved with syndesmophytes. Syndesmophytes are not randomly distributed around the vertebral rim but have preferred locations, which vary with the vertebral level and may be related to biomechanics. Examination of SIJ on abdominal CT scans has found structural changes of sacroiliitis in up to 35% of patients with inflammatory bowel disease. The significance of monosodium urate crystal deposition in the pelvis of axial spondyloarthritis patients without coexisting gout is uncertain. Low-dose CT is a promising tool in AS. Studies of biomarkers or medications and their relations with syndesmophyte progression should take the thoracic spine into account. Abdominal CT scans are useful for detecting changes related to sacroiliitis.

Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS)

The aim of this study was to apply texture analysis (TA) on paraspinal musculature in T2-weighted (T2w) magnetic resonance images (MRI) of symptomatic lumbar spinal stenosis (LSS) patients and correlate the findings with clinical outcome measures. Ninety patients were prospectively enrolled in the multi-centric Lumbar Stenosis Outcome Study (LSOS). All patients received a T2w MRI, from which we selected axial images perpendicular to the intervertebral disc at level L3/4 for TA. Regions-of-interest (ROI) were drawn of the paraspinal musculature and 304 TA features/ ROI were calculated. As clinical outcome measurements, we analysed three commonly applied measures: Spinal Stenosis Measure (SSM), Roland-Morris Disability Questionnaire (RMDQ), as well as the Numeric Rating Scale (NRS). We used two machine learning-based classifiers: Decision table, and k-nearest neighbours (k-NN). We observed no meaningful correlation between TA in paraspinal musculature and the two clinical outcome measures SSM symptoms and SSM function, while a moderate correlation was observed regarding the outcome measures RMDQ (k-NN: r = 0.56) and NRS (Decision Table: r = 0.72). In conclusion, MR TA is a viable tool to quantify medical images and illustrate correlations of microarchitectural changes invisible to a human reader with potential clinical impact. • TA is feasible on paraspinal musculature using MRI. • TA on paraspinal musculature correlates with SSM and RMDQ. • TA may enable a statement regarding clinical impact of imaging findings.

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p < 0.001). Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.