AIMC Topic: Image Interpretation, Computer-Assisted

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IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Medical image analysis
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid ...

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms.

IEEE journal of biomedical and health informatics
Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated re...

Semi-supervised mp-MRI data synthesis with StitchLayer and auxiliary distance maximization.

Medical image analysis
The availability of a large amount of annotated data is critical for many medical image analysis applications, in particular for those relying on deep learning methods which are known to be data-hungry. However, annotated medical data, especially mul...

Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images-Role of Multiscale Decision Aggregation and Data Augmentation.

IEEE journal of biomedical and health informatics
Visual inspection of histopathology images of stained biopsy tissue by expert pathologists is the standard method for grading of prostate cancer (PCa). However, this process is time-consuming and subject to high inter-observer variability. Machine le...

Automated grading of acne vulgaris by deep learning with convolutional neural networks.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variability.

Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks.

Acta ophthalmologica
BACKGROUND: The purpose of this study was to develop an automated diagnosis and quantitative analysis system for plus disease. The system provides a diagnostic decision but also performs quantitative analysis of the typical pathological features of t...

Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data.

IEEE transactions on medical imaging
Prognostic tumor growth modeling via volumetric medical imaging observations can potentially lead to better outcomes of tumor treatment management and surgical planning. Recent advances of convolutional networks (ConvNets) have demonstrated higher ac...

Multi-criterion mammographic risk analysis supported with multi-label fuzzy-rough feature selection.

Artificial intelligence in medicine
CONTEXT AND BACKGROUND: Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential rob...

Hubless keypoint-based 3D deformable groupwise registration.

Medical image analysis
We present a novel algorithm for Fast Registration Of image Groups (FROG), applied to large 3D image groups. Our approach extracts 3D SURF keypoints from images, computes matched pairs of keypoints and registers the group by minimizing pair distances...

A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis.

Circulation. Cardiovascular imaging
BACKGROUND: Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent hum...