Computer-assisted Diagnosis System Research Articles

Detection of antinuclear antibodies: recommendations from EFLM, EASI and ICAP.

Antinuclear antibodies (ANA) are important for the diagnosis of various autoimmune diseases. ANA are usually detected by indirect immunofluorescence assay (IFA) using HEp-2 cells (HEp-2 IFA). There are many variables influencing HEp-2 IFA results, such as subjective visual reading, serum screening dilution, substrate manufacturing, microscope components and conjugate. Newer developments on ANA testing that offer novel features adopted by some clinical laboratories include automated computer-assisted diagnosis (CAD) systems and solid phase assays (SPA). A group of experts reviewed current literature and established recommendations on methodological aspects of ANA testing. This process was supported by a two round Delphi exercise. International expert groups that participated in this initiative included (i) the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group "Autoimmunity Testing"; (ii) the European Autoimmune Standardization Initiative (EASI); and (iii) the International Consensus on ANA Patterns (ICAP). In total, 35 recommendations/statements related to (i) ANA testing and reporting by HEp-2 IFA; (ii) HEp-2 IFA methodological aspects including substrate/conjugate selection and the application of CAD systems; (iii) quality assurance; (iv) HEp-2 IFA validation/verification approaches and (v) SPA were formulated. Globally, 95% of all submitted scores in the final Delphi round were above 6 (moderately agree, agree or strongly agree) and 85% above 7 (agree and strongly agree), indicating strong international support for the proposed recommendations. These recommendations are an important step to achieve high quality ANA testing.

Development of artificial intelligence for the detection and staging of esophageal cancer

: Computer-assisted diagnosis (CAD) using deep learning, based on convolutional neural networks (CNNs), is rapidly developing in modern society and clinical practice. In the medical field of endoscopy, a CAD system can be used in the detection and staging of superficial esophageal cancer. In the detection of superficial esophageal squamous cell carcinoma (ESCC), several studies were reported as using still images of conventional white-light endoscopy and narrow-band imaging (NBI). The sensitivity of CAD systems is over 90%, and in some reports, higher than that of expert endoscopists. In addition, there is a report using video for a validation set that showed good performance. In diagnosing invasion depth, there are reports using conventional white-light imaging, NBI, and magnifying endoscopy with NBI using still images; there are also reports of intrapapillary capillary loop pattern identification using still images. Using white-light endoscopy and NBI, CAD systems showed sensitivity of 84.1–95.4%, specificity of 73.3–79.2%, and accuracy of 80.9–92.9% for differentiating SM1 cancers from SM2 or SM3 cancers in pathology. Additional systems could accurately classify intrapapillary capillary loop patterns as normal or abnormal, with comparable performance to experienced endoscopists. As for esophageal adenocarcinoma (EAC) that arises in Barrett’s esophagus (BE), there are detection reports that show favorable performance. Furthermore, the CAD system from one report identified the location suitable for biopsy; this is helpful for endoscopists because of the difficulty in determining the location of early EAC in BE. Although these studies are still only at research level, excellent performance has been achieved for detecting and staging of superficial esophageal carcinoma by CAD systems. In the near future, CAD systems will support us in detecting and staging esophageal cancers in daily clinical practice, leading to a better prognosis.

딥러닝 기반 위 질환 컴퓨터 보조 진단 시스템 개발 및 Grad-CAM을 활용한 시각화

Gastric cancer ranked first in the incidence of cancer in Korea from 2015 to 2018, and it is a trend that many people suffer from regardless of gender. Because the cause of gastric cancer is not clear and early gastric cancer has no specific symptoms, patients often miss the appropriate treatment time. Gastric endoscopy is the most effective method for diagnosing gastric cancer, but early gastric cancer accompany only subtle changes that are difficult to distinguish with the naked eye, and the diagnosis accuracy may vary depending on the proficiency of the specialist. Therefore, we proposed an EfficientNetV2-L-based Computer-assisted Diagnosis (CADx) system for gastric diseases in this paper. EfficientNetV2 is a network that can have high classification performance for gastric diseases even with a small number of parameters as a model designed using the Training-NAS method. In general, the diversity of data affects the performance of deep learning models. So, we augmented data using Cifar10 augmented policy. Through this method, sensitivity was improved from 0.878 to 0.966 in early gastric cancer and normal classification, and from 0.690 to 0.817 in abnormal and normal classification. In addition, we tried to visualize the region of interest of the model for the location of the lesion by applying Grad-CAM.

PILN: A posterior information learning network for blind reconstruction of lung CT images

Background and objective:Computer tomography (CT) imaging technology has played significant roles in the diagnosis and treatment of various lung diseases, but the degradations in CT images usually cause the loss of detailed structural information and interrupt the judgement from clinicians. Therefore, reconstructing noise-free, high-resolution CT images with sharp details from degraded ones is of great importance for the computer-assisted diagnosis (CAD) system. However, current image reconstruction methods suffer from unknown parameters of multiple degradations in actual clinical images.Methods:To solve these problems, we propose a unified framework, so called Posterior Information Learning Network (PILN), for blind reconstruction of lung CT images. The framework consists of two stages: Firstly, a noise level learning (NLL) network is proposed to quantify the Gaussian and artifact noise degradations into different levels. Inception-residual modules are designed to extract multi-scale deep features from the noisy image, and residual self-attention structures are proposed to refine deep features to essential representations of noise. Secondly, by taking the estimated noise levels as prior information, a cyclic collaborative super-resolution (CyCoSR) network is proposed to iteratively reconstruct the high-resolution CT image and estimate the blur kernel. Two convolutional modules are designed based on cross-attention transformer structure, named as Reconstructor and Parser. The high-resolution image is restored from the degraded image by the Reconstructor under the guidance of the predicted blur kernel, while the blur kernel is estimated by the Parser according to the reconstructed image and the degraded one. The NLL and CyCoSR networks are formulated as an end-to-end framework to handle multiple degradations simultaneously.Results:The proposed PILN is applied to the Cancer Imaging Archive (TCIA) dataset and the Lung Nodule Analysis 2016 Challenge (LUNA16) dataset to evaluate its ability in reconstructing lung CT images. Compared with the state-of-the-art image reconstruction algorithms, it can provide high-resolution images with less noise and sharper details with respect to quantitative benchmarks.Conclusions:Extensive experimental results demonstrate that our proposed PILN can achieve better performance on blind reconstruction of lung CT images, providing noise-free, detail-sharp and high-resolution images without knowing the parameters of multiple degradation sources.

Commentary: Machine learning for autism spectrum disorder diagnosis - challenges and opportunities - a commentary on Schulte-Rüther et al. (2022).

The commentary cites a study by Schulte-Rüther et al. (Journal of Child Psychology and Psychiatry, 2022) that proposed a machine learning model to predict a clinical best-estimate diagnosis of ASD when existing other co-occurring diagnoses. We discuss the valuable contribution of this work to developing a reliable computer-assisted diagnosis (CAD) system for ASD and point out that related research can be integrated with other multimodal machine learning methods. For future studies on developing the CAD system for ASD, we propose problems that need to be solved and potential research directions.

Image synthesis with disentangled attributes for chest X-ray nodule augmentation and detection

Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR images. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the shape/size attributes desired by the detector. To address this issue, we introduce a novel lung nodule synthesis framework in this paper, which decomposes nodule attributes into three main aspects including the shape, the size, and the texture, respectively. A GAN-based Shape Generator firstly models nodule shapes by generating diverse shape masks. The following Size Modulation then enables quantitative control on the diameters of the generated nodule shapes in pixel-level granularity. A coarse-to-fine gated convolutional Texture Generator finally synthesizes visually plausible nodule textures conditioned on the modulated shape masks. Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task. Our experiments demonstrate the enhanced image quality, diversity, and controllability of the proposed lung nodule synthesis framework. We also validate the effectiveness of our data augmentation strategy on greatly improving nodule detection performance.

A Novel Hybrid Deep Learning Framework for Detection and Categorization of Brain Tumor from Magnetic Resonance Images

Cellular abnormality leads to brain tumour formation. This is one of the foremost reasons of adult death all over the world. The typical size of a brain tumour increases within 25 days due to its rapid growth. Early brain tumour diagnosis can save millions of lives. For the purpose of early brain tumour identification, an automatic method is necessary. MRI brain tumour detection improves the survival of patients. Tumour visibility is improved in MRI, which facilitates subsequent treatment. To distinguish between brain MRI images with tumour and images without tumour is suggested in this paper. Many approaches in the field of machine learning including Support Vector Machine, Artificial Neural Networks, and KNN classifier have been developed for solving these issues. But these methods are time consuming, inefficient and require complex procedures. For a Computer Assisted Diagnosis system to aid physicians and radiologists in the identification and categorization of tumours, artificial intelligence is used. Deep learning has demonstrated an encouraging efficiency in computer vision systems over the past decade. In this paper, identification and classification of brain tumour from MR images employing BGWO-CNN-LSTM method is proposed. The proposed method on a testing set with 6100 MRI images of four different kinds of brain tumours is utilized. In comparison to earlier research on the same data set, the suggested approach achieved 99.74% accuracy, 99.23% recall and 99.54% specificity which are greater than the other techniques.

A Computer-Assisted Diagnosis System for the Detection of Chronic Gastritis in Endoscopic Images Using A Novel Convolution and Relative Self-Attention Parallel Network

A Computer-Assisted Diagnosis System for the Detection of Chronic Gastritis in Endoscopic Images Using A Novel Convolution and Relative Self-Attention Parallel Network

Effective lung cancer detection using deep learning network

The use of a computer-assisted diagnosis system was crucial to the results of the clinical study conducted to determine the nature of the human illness. When compared to other disorders, lung cancer requires extra caution during the examination process. This is because the mortality rate from lung cancer is higher because it affects both men and women. Poor image resolution has hampered previous lung cancer detection technologies, preventing them from achieving the requisite degree of dependability. Therefore, in this study, we provide a unique approach to lung cancer prognosis that makes use of improved machine learning and processing of images. Images of lung disease from CT scan databases created using quasi cells are used for diagnosis. Multilayer illumination was used to analyse the generated images, which improved the precision of the lungs' depiction by probing each and every one of their pixels while simultaneously decreasing the amount of background noise. Lung CT images are pre-processed to remove noise, and then a more advanced deep learning network is used to isolate the affected region. The territory is partitioned into subnetworks according to the number of existing networks, from which different features are subsequently extracted. Next, an ensemble classifier should be used to correctly diagnose lung diseases. Using MATLAB simulation, the authors examine how the provided technique improves the rate at which lung cancer could be diagnosed.

Multi-Label Softmax Networks for Pulmonary Nodule Classification Using Unbalanced and Dependent Categories.

Radiographic attributes of lung nodules remedy the shortcomings of lung cancer computer-assisted diagnosis systems, which provides interpretable diagnostic reference for doctors. However, current studies fail to dedicate multi-label classification of lung nodules using convolutional neural networks (CNNs) and are inferior in exploiting statistical dependency between the labels. In addition, data imbalance is an indispensable problem to be reckoned with when employing CNNs to perform lung nodule classification. It introduces greater challenges especially in the multi-label classification. In this paper, we propose a method called MLSL-Net to discriminate lung nodule characteristics and simultaneously address the challenges. Particularly, the proposal employs multi-label softmax loss (MLSL) as the performance index, aiming to reduce the ranking errors between the labels and within the labels during training, thereby optimizing ranking loss and AUC directly. Such criterions can better evaluate the classifier's performance on the multi-label imbalanced dataset. Furthermore, a scale factor is introduced based on the investigation of the max surrogate function. Different from preceding usages, the small factor is used so that to narrow the discrepancy of gradients produced by different labels. More interestingly, this factor also facilitates the exploit of label dependency. Experimental results on the LIDC-IDRI dataset as well as another akin dataset demonstrate that MLSL-Net can effectively perform multi-label classification despite the imbalance issue. Meanwhile, the results confirm the responsibility of the factor for capturing label correlations, accordingly leading to more accurate predictions.

Assessment of Enhancement Kinetics Improves the Specificity of Abbreviated Breast MRI: Performance in an Enriched Cohort

Objective: To investigate the added value of kinetic information for breast lesion evaluation on abbreviated breast MRI (AB-MRI). Methods: This retrospective study analyzed 207 breast lesions with Breast Imaging Reporting and Data System categories 3, 4, or 5 on AB-MRI in 198 consecutive patients who had breast MRI for screening after breast cancer surgery between January 2017 and December 2019. All lesions were pathologically confirmed or stable on follow-up images for 2 years or more. Kinetic information of the lesions regarding the degree and rate of enhancement on the first post-contrast-enhanced image and the enhancement curve type from two post-contrast-enhanced images were analyzed on a commercially available computer-assisted diagnosis system. The diagnostic performances of AB-MRI with morphological analysis alone and with the addition of kinetic information were compared using the McNemar test. Results: Of 207 lesions, 59 (28.5%) were malignant and 148 (71.5%) were benign. The addition of an enhancement degree of ≥90% to the morphological analysis significantly increased the specificity of AB-MRI (29.7% vs. 52.7%, p < 0.001) without significantly reducing the sensitivity (94.9% vs. 89.8%, p = 0.083) compared to morphological analysis alone. Unnecessary biopsy could have been avoided in 34 benign lesions, although three malignant lesions could have been missed. For detecting invasive cancer, adding an enhancement degree ≥107% to the morphological analysis significantly increased the specificity (26.5% vs. 57.6%, p < 0.001) without significantly decreasing the sensitivity (94.6% vs. 86.5%, p = 0.083). Conclusion: Adding the degree of enhancement on the first post-contrast-enhanced image to the morphological analysis resulted in higher AB-MRI specificity without compromising its sensitivity.

Automatic No-Reference kidney tissue whole slide image quality assessment based on composite fusion models

The visual quality of digitized whole slide images (WSIs) for kidney tissue not only affects the diagnosis and subsequent treatment, but also has a decisive impact on the accuracy of multiclass segmentation, classification and object detection during computer intelligent analysis. Currently, pathologists usually assess image quality through eye screening, which greatly relies on the pathologist experience and brings about subjectivity and non-repeatability issues. In this paper, we develop a no-reference image quality assessment framework including a fused CNN classification module, a quality score conversion module and a comprehensive quality prediction module, which automatically classifies WSIs of kidney tissue into four quality levels: excellent, good, average, and poor, and calculates a rough quality score. The original image and the regions of interest are combined and fused to comprehensively evaluate the quality of a WSI through multiple factors instead of a simple deep learning network. Extensive experiments conducted on our in-house dataset confirm that our proposed framework obtains satisfactory results with an accuracy of 90.05%, surpassing the performance of the typical image quality assessment models, and achieves the level of junior pathologist. Therefore, our proposed method can be embedded into a computer assisted diagnosis system to help pathologists in analysis of histopathological images and judgment of reliability of the images. The source code and trained models will be available at https://github.com/kidneyPathology/WSIQA.

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Medical image processing involves using and examining 3D human body images, which are most frequently acquired through a computed tomography scanner, to diagnose disorders. Medical image process- ing helps radiologists, engineers, and clinicians better comprehend the anatomy of specific patients or groups of patients. Due to recent advancements in deep learn ing techniques, the study of medical image analysis is now a quickly expanding area of research. Interstitial Lung Disease is a chronic lung disease that worsens with time. This condition cannot be completely treated when the lungs have been damaged. Early detection, on the other hand, aids in the control of the disease. It causes lung scarring as a result. The first methodology characterizes lung tissue utilizing first order statistics, grey live occurrence, run length matrices, and fractal analysis. It was suggested by Uppaluri et al in one instance. In the pre-processing step, patients' CT scans are presented using various color map models for better understanding of data-set. and also for determining the patients final Force Vital Capacity and Confidence values using a Pytorch model with leaky relu activation function. These variables can be used to determine whether a person has a disease. Segmentation is a crucial stage in employing a computer assisted diagnosis system to estimate interstitial lung disease. Accurate segmentation of aberrant lung is essential for a trustworthy computer-aided illness diagnosis. Using separate training, validation, and test sets, we proposed an efficient deep learning model using Unet architecture and Densenet121 to segment lungs with Interstitial Lung Disease. The proposed segmentation model distinguishes the exact lung region from the ct slice background. To train and evaluate the algo rithm, 176 sparsely annotated Computed Tomography scans were utilized. The training was completed in a supervised and end to end manner. Contrary to current approaches, the suggested method yields accurate segmentation results without the requirement for re-initialization. We were able to achieve an accuracy of 92.59 percent after training the proposed model with Nvidia's CUDA GPU.

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy.

Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques.

Coronavirus disease, frequently referred to as COVID-19, is a contagious and transmittable disease produced by the SARS-CoV-2 virus. The only solution to tackle this virus and reduce its spread is early diagnosis. Pathogenic laboratory tests such as the polymerase chain reaction (PCR) process take a long time. Also, they regularly produce incorrect results. However, they are still considered the critical standard for detecting the virus. Hence, there is a solid need to evolve computer-assisted diagnosis systems capable of providing quick and low-cost testing in areas where traditional testing procedures are not feasible. This study focuses on COVID-19 detection using X-ray images. The prime objective is to introduce a computer-assisted diagnosis (CAD) system to differentiate COVID-19 from healthy and pneumonia cases using X-ray image sequences. This work utilizes standard transfer-learning techniques for COVID-19 detection. It proposes the master-slave architecture using the most state-of-the-art Densenet201 and Squeezenet1_0 techniques for classifying the COVID-19 virus in chest X-ray image sequences. This paper compares the proposed models with other standard transfer-learning approaches for COVID-19. The performance metrics demonstrate that the proposed approach outperforms standard transfer-learning approaches. This research also fine-tunes hyperparameters and predicts the optimized learning rate to achieve the highest accuracy in the model. After fine-tuning the learning rate, the DenseNet201 model retrieves an accuracy of 83.33%, while the fastest model is SqueezeNet1_0, which retrieves an accuracy of 80%.

ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence.

Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM's visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps.

Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma.

BACKGROUNDUpper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma (ESCC) detection; however, endoscopists require long-term training to avoid missing superficial lesions. AIMTo develop a deep learning computer-assisted diagnosis (CAD) system for endoscopic detection of superficial ESCC and investigate its application value. METHODSWe configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm. A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets, respectively. Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance. The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTSThe area under the receiver operating characteristics curve, accuracy, sensitivity, and specificity of the CAD system were 0.982 [95% confidence interval (CI): 0.969-0.994], 92.9% (95%CI: 89.5%-95.2%), 91.9% (95%CI: 87.4%-94.9%), and 94.7% (95%CI: 89.0%-97.6%), respectively. The accuracy of CAD was significantly higher than that of non-expert endoscopists (78.3%, P < 0.001 compared with CAD) and comparable to that of expert endoscopists (91.0%, P = 0.129 compared with CAD). After referring to the CAD results, the accuracy of the non-expert endoscopists significantly improved (88.2% vs 78.3%, P < 0.001). Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system. CONCLUSIONThe diagnostic performance of the CAD system is promising and may assist in improving detectability, particularly for inexperienced endoscopists.

Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?

Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.

A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis.

One of the primary difficulties in treating patients with genetic syndromes is diagnosing their condition. Many syndromes are associated with characteristic facial features that can be imaged and utilized by computer-assisted diagnosis systems. In this work, we develop a novel 3D facial surface modeling approach with the objective of maximizing diagnostic model interpretability within a flexible deep learning framework. Therefore, an invertible normalizing flow architecture is introduced to enable both inferential and generative tasks in a unified and efficient manner. The proposed model can be used (1) to infer syndrome diagnosis and other demographic variables given a 3D facial surface scan and (2) to explain model inferences to non-technical users via multiple interpretability mechanisms. The model was trained and evaluated on more than 4700 facial surface scans from subjects with 47 different syndromes. For the challenging task of predicting syndrome diagnosis given a new 3D facial surface scan, age, and sex of a subject, the model achieves a competitive overall top-1 accuracy of 71%, and a mean sensitivity of 43% across all syndrome classes. We believe that invertible models such as the one presented in this work can achieve competitive inferential performance while greatly increasing model interpretability in the domain of medical diagnosis.

Extract roi from mammogram images using morphological and k-means clustering

Cancer is leading cause of death in worldwide. Cancer cells damages all cells surrounded by it and hence covers the complete area of the body. Amongst women breast cancer is most common deadly disease as compared to men. In any type of cancer early-stage detection plays vital role as it can save the patient’s life. If cancer is diagnosed early by using breast self-examination (BSE) and clinical breast examination (CBE) at 40-49 years of age, the survival rate of breast cancer reaches 100 per cent. New strategies named CAD (computer-assisted diagnosis) programs for early detection utilizing multiple mammogram datasets, such as mini-mias, DDSM, etc., CAD (Computer Aided Diagnosis) systems are mostly used for the second opinion for radiologists. Many researchers are already developing different CAD systems. Different databases are available for the researchers to study and detect the cancerous tumors. In this paper Mini-Mias database is used. Mammogram images are low contrast and may contain noise. Filtering or pre-processing is required to remove any noises present in the image, here Weiner filter is used. Two methodologies are compared here. First using morphological operated segmentation and second is by using k-means clustering algorithm.