Quadratic Weighted Kappa Score Research Articles

A multifaceted architecture to Automate Essay Scoring for assessing english article writing: Integrating semantic, thematic, and linguistic representations

We propose a multifaceted architecture for automated essay scoring integrating semantic, thematic, and linguistic representations. Specifically, for the deep semantic content of the essay, we employ Convolutional Neural Network (ConvNet) and attention mechanism module to extract local semantic representation, and use ConvNet, Long Short-Term Memory network (LSTM), and attention mechanism module to capture the global semantic representation, providing the comprehensive deep semantic-facet representations. To answer the presence of prompt in the test essay, we utilize the Doc2Vec model to represent the target essay and the essay prompt as vectors and calculate their textual similarity as thematic-facet representations. In addition, we construct six categories of nine manual features, including grammatical errors, essay length, textual complexity, word complexity, sentence complexity, and the number of clauses, to capture the shallow linguistic representation that deep learning models may struggle to uncover. Finally, we integrate the multifaceted representation through the fully connected layer to automatically score essays. Experimental results on the standard public dataset ASAP and the more complex dataset CLC-FCE, which varies in standards, lengths, and topics, demonstrate that the proposed method effectively captures diverse aspects of textual representations. For the ASAP dataset, our method reduced the training time to 1862 s per essay and the inference time to 0.9778 s, achieving a 1.13 improvement in the Quadratic Weighted Kappa score over the previous best method. On the CLC-FCE dataset, our approach enhanced the Pearson's product-moment correlation coefficient and Spearman's rank correlation coefficient by 0.005 and 0.0337 respectively, surpassing prior benchmarks.

Coherence‐based automatic short answer scoring using sentence embedding

AbstractAutomatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy. However, the actual goals still need to be attained, like embedding essays into vectors with cohesion and coherence, and providing student feedback is still challenging. In this paper, we proposed coherence‐based embedding of an essay into vectors using sentence‐Bidirectional Encoder Representation for Transformers. We trained these vectors on Long short‐term memory and bidirectional long short‐term memory to capture sentence connectivity with other sentences' semantics. We used two datasets: standard ASAP Kaggle and a domain‐specific dataset with almost 2500 responses from 650 students. Our model performed well on both datasets, with an average quadratic weighted kappa score of 0.76. Furthermore, we achieved good results compared to other prescribed models, and we also tested our model on adversarial responses of both datasets and observed decent outcomes.

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.

Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.

Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease.

Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson's disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson's disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson's disease. In addition, the features attributed to the mild cognitive impairment in Parkinson's disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson's disease.

Development of a deep learning algorithm for myopic maculopathy classification based on OCT images using transfer learning.

PurposeTo apply deep learning (DL) techniques to develop an automatic intelligent classification system identifying the specific types of myopic maculopathy (MM) based on macular optical coherence tomography (OCT) images using transfer learning (TL).MethodIn this retrospective study, a total of 3,945 macular OCT images from 2,866 myopic patients were recruited from the ophthalmic outpatients of three hospitals. After culling out 545 images with poor quality, a dataset containing 3,400 macular OCT images was manually classified according to the ATN system, containing four types of MM with high OCT diagnostic values. Two DL classification algorithms were trained to identify the targeted lesion categories: Algorithm A was trained from scratch, and algorithm B using the TL approach initiated from the classification algorithm developed in our previous study. After comparing the training process, the algorithm with better performance was tested and validated. The performance of the classification algorithm in the test and validation sets was evaluated using metrics including sensitivity, specificity, accuracy, quadratic-weighted kappa score, and the area under the receiver operating characteristic curve (AUC). Moreover, the human-machine comparison was conducted. To better evaluate the algorithm and clarify the optimization direction, the dimensionality reduction analysis and heat map analysis were also used to visually analyze the algorithm.ResultsAlgorithm B showed better performance in the training process. In the test set, the algorithm B achieved relatively robust performance with macro AUC, accuracy, and quadratic-weighted kappa of 0.986, 96.04% (95% CI: 0.951, 0.969), and 0.940 (95% CI: 0.909–0.971), respectively. In the external validation set, the performance of algorithm B was slightly inferior to that in the test set. In human-machine comparison test, the algorithm indicators were inferior to the retinal specialists but were the same as the ordinary ophthalmologists. In addition, dimensionality reduction visualization and heatmap visualization analysis showed excellent performance of the algorithm.ConclusionOur macular OCT image classification algorithm developed using the TL approach exhibited excellent performance. The automatic diagnosis system for macular OCT images of MM based on DL showed potential application prospects.

Detection of Diabetic Retinopathy Using Deep Learning

Abstract: Diabetic retinopathy is one of the most dangerous complications of diabetes, leading to permanent blindness if left untreated. One of the major challenges is early detection, which is very important for the success of treatment. Unfortunately, accurate identification of the stage of diabetic retinopathy is notoriously tricky and requires expert human interpretation of fundus images. Simplifying the detection step is essential and can help millions of people. Convolutional Neural Networks (CNNs) have been successfully used in many neighboring subjects and for the diagnosis of diabetic retinopathy itself. However, the high cost of large annotated datasets as well as inconsistencies between different clinicians hinders the implementation of these methods. In this paper, we propose an automatic method based on deep learning to detect the stage of diabetic retinopathy using a single human fundus image. In addition, we propose a multi-stage transfer learning approach that uses similar datasets with different labels. The presented method can be used as a screening method for the early detection of diabetic retinopathy with a sensitivity and specificity of 0.99 and is ranked 54 out of 2943 competing methods (quadratic weighted kappa score 0.925466) on the APTOS 2019 Blindness Detection Dataset (13,000 images).

Adversarial training for prostate cancer classification using magnetic resonance imaging.

To use adversarial training to increase the generalizability and diagnostic accuracy of deep learning models for prostate cancer diagnosis. This multicenter study retrospectively included 396 prostate cancer patients who underwent magnetic resonance imaging (development set, 297 patients from Shanghai Jiao Tong University Affiliated Sixth People's Hospital and Eighth People's Hospital; test set, 99 patients from Renmin Hospital of Wuhan University). Two binary classification deep learning models for clinically significant prostate cancer classification [PM1, pretraining Visual Geometry Group network (VGGNet)-16-based model 1; PM2, pretraining residual network (ResNet)-50-based model 2] and two multiclass classification deep learning models for prostate cancer grading (PM3, pretraining VGGNet-16-based model 3; PM4: pretraining ResNet-50-based model 4) were built using apparent diffusion coefficient and T2-weighted images. These models were then retrained with adversarial examples starting from the initial random model parameters (AM1, adversarial training VGGNet-16 model 1; AM2, adversarial training ResNet-50 model 2; AM3, adversarial training VGGNet-16 model 3; AM4, adversarial training ResNet-50 model 4, respectively). To verify whether adversarial training can improve the diagnostic model's effectiveness, we compared the diagnostic performance of the deep learning methods before and after adversarial training. Receiver operating characteristic curve analysis was performed to evaluate significant prostate cancer classification models. Differences in areas under the curve (AUCs) were compared using Delong's tests. The quadratic weighted kappa score was used to verify the PCa grading models. AM1 and AM2 had significantly higher AUCs than PM1 and PM2 in the internal validation dataset (0.84 vs. 0.89 and 0.83 vs. 0.87) and test dataset (0.73 vs. 0.86 and 0.72 vs. 0.82). AM3 and AM4 showed higher κ values than PM3 and PM4 in the internal validation dataset {0.266 [95% confidence interval (CI): 0.152-0.379] vs. 0.292 (95% CI: 0.178-0.405) and 0.254 (95% CI: 0.159-0.390) vs. 0.279 (95% CI: 0.163-0.396)} and test set [0.196 (95% CI: 0.029-0.362) vs. 0.268 (95% CI: 0.109-0.427) and 0.183 (95% CI: 0.015-0.351) vs. 0.228 (95% CI: 0.068-0.389)]. Using adversarial examples to train prostate cancer classification deep learning models can improve their generalizability and classification abilities.

Deep Learning for Diabetic Retinopathy Disease

Diabetic Retinopathy (DR) is a human eye disease that affects diabetics and damages their retina, potentially leading to blindness in the long term. DR has been manually tested by ophthalmologists until now and is a time- consuming process. As a result, this job (project) will focus on analyzing various DR levels using Deep Learning (DL), which is a derivative of Artificial Intelligence (AI). We used an enormous dataset of around 3662 train images to train a model called DenseNet to automatically detect the DR point, which is then categorized into high resolution fundus images. The dataset we're using is free to use on Kaggle. There are five different DR stages: 0, 1, 2, 3, and 4. the input parameters in this paper are the patient's fundus eye pictures. The features of fundus images of the eye will be extracted by a qualified model (DenseNet Architecture), and then the activation function will provide the output. The precision of this architecture for DR detection was 0.9611 (quadratic weighted kappa score of 0.8981). Finally, we compare the two CNN architectures, the VGG16 architecture and the DenseNet121 architecture.

Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images

Early detection and treatment of diabetic retinopathy (DR) can significantly reduce the risk of vision loss in patients. In essence, we are faced with two challenges: (i) how to simultaneously achieve domain adaptation from the different domains and (ii) how to build an interpretable multi-instance learning (MIL) on the target domain in an end-to-end framework. In this paper, we address these issues and propose a unified weakly-supervised domain adaptation framework, which consists of three components: domain adaptation, instance progressive discriminator and multi-instance learning with attention. The method models the relationship between the patches and images in the target domain with a multi-instance learning scheme and an attention mechanism. Meanwhile, it incorporates all available information from both source and target domains for a jointly learning strategy. We validate the performance of the proposed framework for DR grading on the Messidor dataset and the large-scale Eyepacs dataset. The experimental results demonstrate that it achieves an average accuracy of 0.949 (95% CI 0.931–0.958)/0.764 (95% CI 0.755–0.772) and an average AUC value of 0.958 (95% CI 0.945–0.962)/0.749 (95% CI 0.732–0.761) for binary-class/multi-class classification tasks on the Messidor dataset. Moreover, the proposed method achieves an accuracy of 0.887 and a quadratic weighted kappa score value of 0.860 on the Eyepacs dataset, outperforming the state-of-the-art approaches. Comprehensive experiments confirm the effectiveness of the approach in terms of both grading performance and interpretability. The source code is available at https://github.com/HouQingshan/WAD-Net.

Necessity of Local Modification for Deep Learning Algorithms to Predict Diabetic Retinopathy.

Deep learning (DL) algorithms are used to diagnose diabetic retinopathy (DR). However, most of these algorithms have been trained using global data or data from patients of a single region. Using different model architectures (e.g., Inception-v3, ResNet101, and DenseNet121), we assessed the necessity of modifying the algorithms for universal society screening. We used the open-source dataset from the Kaggle Diabetic Retinopathy Detection competition to develop a model for the detection of DR severity. We used a local dataset from Taipei City Hospital to verify the necessity of model localization and validated the three aforementioned models with local datasets. The experimental results revealed that Inception-v3 outperformed ResNet101 and DenseNet121 with a foreign global dataset, whereas DenseNet121 outperformed Inception-v3 and ResNet101 with the local dataset. The quadratic weighted kappa score () was used to evaluate the model performance. All models had 5–8% higher κ for the local dataset than for the foreign dataset. Confusion matrix analysis revealed that, compared with the local ophthalmologists’ diagnoses, the severity predicted by the three models was overestimated. Thus, DL algorithms using artificial intelligence based on global data must be locally modified to ensure the applicability of a well-trained model to make diagnoses in clinical environments.

Automatic Severity Classification of Diabetic Retinopathy Based on DenseNet and Convolutional Block Attention Module

Diabetic Retinopathy (DR) - a complication developed due to heightened blood glucose levels- is deemed one of the most sight-threatening diseases. Unfortunately, DR screening is manually acquired by an ophthalmologist, a process that can be considered erroneous and time-consuming. Accordingly, automated DR diagnostics have become a focus of research in recent years due to the tremendous increase in diabetic patients. Moreover, the recent accomplishments demonstrated by Convolutional Neural Networks (CNN) settle them as state-of-the-art for DR stage identification. This paper proposes a new automatic deep-learning-based approach for severity detection by utilizing a single Color Fundus photograph (CFP). The proposed technique employs DenseNet169’s encoder to construct a visual embedding. Furthermore, Convolutional Block Attention Module (CBAM) is introduced on top of the encoder to reinforce its discriminative power. Finally, the model is trained using cross-entropy loss on the Kaggle Asia Pacific Tele-Ophthalmology Society’s (APTOS) dataset. On the binary classification task, we accomplished (97% accuracy - 97% sensitivity - 98.3% specificity - 0.9455, Quadratic Weighted Kappa score (QWK)) compared to the state-of-the-art. Moreover, Our network showed high competency (82% accuracy - 0.888 (QWK)) for severity grading. The significant contribution of the proposed framework is that it efficiently grades the severity level of diabetic retinopathy while reducing the time and space complexity required, which demonstrates it as a promising candidate for autonomous diagnosis.

Vision Transformer‐based recognition of diabetic retinopathy grade

In the domain of natural language processing, Transformers are recognized as state-of-the-art models, which opposing to typical convolutional neural networks (CNNs) do not rely on convolution layers. Instead, Transformers employ multi-head attention mechanisms as the main building block to capture long-range contextual relations between image pixels. Recently, CNNs dominated the deep learning solutions for diabetic retinopathy grade recognition. However, spurred by the advantages of Transformers, we propose a Transformer-based method that is appropriate for recognizing the grade of diabetic retinopathy. The purposes of this work are to demonstrate that (i) the pure attention mechanism is suitable for diabetic retinopathy grade recognition and (ii) Transformers can replace traditional CNNs for diabetic retinopathy grade recognition. This paper proposes a Vision Transformer-based method to recognize the grade of diabetic retinopathy. Fundus images are subdivided into non-overlapping patches, which are then converted into sequences by flattening, and undergo a linear and positional embedding process to preserve positional information. Then, the generated sequence is input into several multi-head attention layers to generate the final representation. The first token sequence is input to a softmax classification layer to produce the recognition output in the classification stage. The dataset for training and testing employs fundus images of different resolutions, subdivided into patches. We challenge our method against current CNNs and extreme learning machines and achieve an appealing performance. Specifically, the suggested deep learning architecture attains an accuracy of 91.4%, specificity=0.977 (95% confidence interval (CI) (0.951-1)), precision=0.928 (95% CI (0.852-1)), sensitivity=0.926 (95% CI (0.863-0.989)), quadratic weighted kappa score=0.935, and area under curve (AUC)=0.986. Our comparative experiments against current methods conclude that our model is competitive and highlight that an attention mechanism based on a Vision Transformer model is promising for the diabetic retinopathy grade recognition task.

Inter-method reliability of the modified Rankin Scale in patients with subarachnoid hemorrhage

Background and objectivesThe modified Rankin Scale (mRS) is one of the most frequently used outcome measures in trials in patients with an aneurysmal subarachnoid hemorrhage (aSAH). The assessment method of the mRS is often not clearly described in trials, while the method used might influence the mRS score. The aim of this study is to evaluate the inter-method reliability of different assessment methods of the mRS.MethodsThis is a prospective, randomized, multicenter study with follow-up at 6 weeks and 6 months. Patients aged ≥ 18 years with aSAH were randomized to either a structured interview or a self-assessment of the mRS. Patients were seen by a physician who assigned an mRS score, followed by either the structured interview or the self-assessment. Inter-method reliability was assessed with the quadratic weighted kappa score and percentage of agreement. Assessment of feasibility of the self-assessment was done by a feasibility questionnaire.ResultsThe quadratic weighted kappa was 0.60 between the assessment of the physician and structured interview and 0.56 between assessment of the physician and self-assessment. Percentage agreement was, respectively, 50.8 and 19.6%. The assessment of the mRS through a structured interview and by self-assessment resulted in systematically higher mRS scores than the mRS scored by the physician. Self-assessment of the mRS was proven feasible.DiscussionThe mRS scores obtained with different assessment methods differ significantly. The agreement between the scores is low, although the reliability between the assessment methods is good. This should be considered when using the mRS in clinical trials.Trial registrationwww.trialregister.nl; Unique identifier: NL7859.

AI-Model for Identifying Pathologic Myopia Based on Deep Learning Algorithms of Myopic Maculopathy Classification and "Plus" Lesion Detection in Fundus Images.

Background: Pathologic myopia (PM) associated with myopic maculopathy (MM) and “Plus” lesions is a major cause of irreversible visual impairment worldwide. Therefore, we aimed to develop a series of deep learning algorithms and artificial intelligence (AI)–models for automatic PM identification, MM classification, and “Plus” lesion detection based on retinal fundus images.Materials and Methods: Consecutive 37,659 retinal fundus images from 32,419 patients were collected. After excluding 5,649 ungradable images, a total dataset of 32,010 color retinal fundus images was manually graded for training and cross-validation according to the META-PM classification. We also retrospectively recruited 1,000 images from 732 patients from the three other hospitals in Zhejiang Province, serving as the external validation dataset. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and quadratic-weighted kappa score were calculated to evaluate the classification algorithms. The precision, recall, and F1-score were calculated to evaluate the object detection algorithms. The performance of all the algorithms was compared with the experts’ performance. To better understand the algorithms and clarify the direction of optimization, misclassification and visualization heatmap analyses were performed.Results: In five-fold cross-validation, algorithm I achieved robust performance, with accuracy = 97.36% (95% CI: 0.9697, 0.9775), AUC = 0.995 (95% CI: 0.9933, 0.9967), sensitivity = 93.92% (95% CI: 0.9333, 0.9451), and specificity = 98.19% (95% CI: 0.9787, 0.9852). The macro-AUC, accuracy, and quadratic-weighted kappa were 0.979, 96.74% (95% CI: 0.963, 0.9718), and 0.988 (95% CI: 0.986, 0.990) for algorithm II. Algorithm III achieved an accuracy of 0.9703 to 0.9941 for classifying the “Plus” lesions and an F1-score of 0.6855 to 0.8890 for detecting and localizing lesions. The performance metrics in external validation dataset were comparable to those of the experts and were slightly inferior to those of cross-validation.Conclusion: Our algorithms and AI-models were confirmed to achieve robust performance in real-world conditions. The application of our algorithms and AI-models has promise for facilitating clinical diagnosis and healthcare screening for PM on a large scale.

Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning

The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3–82.7%), the Cohen’s kappa score (κ) was 0.650 (95% CI: 0.570–0.730), and the quadratic-weighted kappa score (κquad) was 0.897 (95% CI: 0.815–0.979). When trained on 621 cases from one institution and validated on 167 cases from the other institution, the system’s accuracy reached 67.4% (95% CI: 63.2–71.6%), κ 0.553 (95% CI: 0.495–0.610), and the κquad 0.880 (95% CI: 0.822–0.938). In order to evaluate the impact of the proposed method, performance comparison with several baseline methods was also performed. While limited by case volume and a few more factors, the results of this study can contribute to the potential development of an artificial intelligence system to diagnose other cancers without extensive region-level annotations.

A convolutional neural network for the screening and staging of diabetic retinopathy.

Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) and fundus photography for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars. Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease. Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (i.e. no DR), moderate DR (i.e. a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (i.e. a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 0.91–0.92 when both 5-fold, and 10-fold cross validation methods were used respectively. A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used. The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care. This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss.

Automated grading of prostate cancer using convolutional neural network and ordinal class classifier

Abstract Prostate Cancer (PCa) is one of the most prominent cancer among men. Early diagnosis and treatment planning are significant in reducing the mortality rate due to PCa. Accurate prediction of grade is required to ensure prompt treatment for cancer. Grading of prostate cancer can be considered as an ordinal class classification problem. This paper presents a novel method for the grading of prostate cancer from multiparametric magnetic resonance images using VGG-16 Convolutional Neural Network and Ordinal Class Classifier with J48 as the base classifier. Multiparametric magnetic resonance images of the PROSTATEx-2 2017 grand challenge dataset are employed for this work. The method achieved a moderate quadratic weighted kappa score of 0.4727 in the grading of PCa into 5 grade groups, which is higher than state-of-the-art methods. The method also achieved a positive predictive value of 0.9079 in predicting clinically significant prostate cancer.

Interrater Agreement between Nurses for the Pediatric Canadian Triage and Acuity Scale in a Tertiary Care Center

The objective was to measure the interrater agreement between nurses assigning triage levels to children visiting a pediatric emergency departments (EDs) assisted by a computerized version of the Pediatric Canadian Triage and Acuity Scale (PedCTAS). This was a prospective cohort study evaluating children triaged from Level 2 (emergent) to Level 5 (nonurgent). A convenience sample of patients triaged during 38 shifts from April to September 2007 in a tertiary care pediatric ED was evaluated. All patients were initially triaged by regular triage nurses using a computerized version of the PedCTAS. Research nurses performed a second evaluation blinded to the first evaluation using the same triage tool. These research nurses were regular ED nurses performing extra hours for research purposes exclusively. The primary outcome measure was the interrater agreement between the two nurses as measured by the linear weighted kappa score. Secondary outcomes included the proportion of patient for which nurses did not apply the triage level suggested by Staturg (override) and agreement for these overrides. A total of 499 patients were recruited. The overall interrater agreement was moderate (linear weighted kappa score of 0.55 [95% confidence interval {CI} = 0.48 to 0.61] and quadratic weighted kappa score of 0.61 [95% CI = 0.42 to 0.80]). There was a discrepancy of more than one level in only 10 patients (2% of the study population). Overrides occurred in 23.2 and 21.8% for regular and research triage nurses, respectively. These overrides were equally distributed between increase and decrease in triage level. Nurses using Staturg, which is a computerized version of the PedCTAS, demonstrated moderate interrater agreement for assignment of triage level to children presenting to a pediatric ED.

MOST PIGMENT PRICES FAIL TO KEEP PACE WITH INFLATION WHILE CARBON BLACK PRICES RISE TWICE AS FAST

Early detection and treatment of diabetic retinopathy (DR) can significantly reduce the risk of vision loss in patients. In essence, we are faced with two challenges: (i) how to simultaneously achieve domain adaptation from the different domains and (ii) how to build an interpretable multi-instance learning (MIL) on the target domain in an end-to-end framework. In this paper, we address these issues and propose a unified weakly-supervised domain adaptation framework, which consists of three components: domain adaptation, instance progressive discriminator and multi-instance learning with attention. The method models the relationship between the patches and images in the target domain with a multi-instance learning scheme and an attention mechanism. Meanwhile, it incorporates all available information from both source and target domains for a jointly learning strategy. We validate the performance of the proposed framework for DR grading on the Messidor dataset and the large-scale Eyepacs dataset. The experimental results demonstrate that it achieves an average accuracy of 0.949 (95% CI 0.931–0.958)/0.764 (95% CI 0.755–0.772) and an average AUC value of 0.958 (95% CI 0.945–0.962)/0.749 (95% CI 0.732–0.761) for binary-class/multi-class classification tasks on the Messidor dataset. Moreover, the proposed method achieves an accuracy of 0.887 and a quadratic weighted kappa score value of 0.860 on the Eyepacs dataset, outperforming the state-of-the-art approaches. Comprehensive experiments confirm the effectiveness of the approach in terms of both grading performance and interpretability. The source code is available at https://github.com/HouQingshan/WAD-Net.