Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self …
Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
Self-path: Self-supervision for classification of pathology images with limited annotations
NA Koohbanani, B Unnikrishnan… - … on Medical Imaging, 2021 - ieeexplore.ieee.org
While high-resolution pathology images lend themselves well to 'data hungry'deep learning
algorithms, obtaining exhaustive annotations on these images for learning is a major …
algorithms, obtaining exhaustive annotations on these images for learning is a major …
High-accuracy prostate cancer pathology using deep learning
Deep learning (DL) is a powerful methodology for the recognition and classification of tissue
structures in digital pathology. Its performance in prostate cancer pathology is still under …
structures in digital pathology. Its performance in prostate cancer pathology is still under …
A multi-resolution model for histopathology image classification and localization with multiple instance learning
Large numbers of histopathological images have been digitized into high resolution whole
slide images, opening opportunities in developing computational image analysis tools to …
slide images, opening opportunities in developing computational image analysis tools to …
Probabilistic modeling of inter-and intra-observer variability in medical image segmentation
A Schmidt, P Morales-Alvarez… - Proceedings of the …, 2023 - openaccess.thecvf.com
Medical image segmentation is a challenging task, particularly due to inter-and intra-
observer variability, even between medical experts. In this paper, we propose a novel …
observer variability, even between medical experts. In this paper, we propose a novel …
Path R-CNN for prostate cancer diagnosis and gleason grading of histological images
Prostate cancer is the most common and second most deadly form of cancer in men in the
United States. The classification of prostate cancers based on Gleason grading using …
United States. The classification of prostate cancers based on Gleason grading using …
A review of artificial intelligence in prostate cancer detection on imaging
I Bhattacharya, YS Khandwala… - … advances in urology, 2022 - journals.sagepub.com
A multitude of studies have explored the role of artificial intelligence (AI) in providing
diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection …
diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection …
Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis
Background: Deep convolutional neural networks (CNNs) have yielded promising results in
automatic whole slide images (WSIs) processing for digital pathology in recent years …
automatic whole slide images (WSIs) processing for digital pathology in recent years …
Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints
Breast tumor segmentation is an important step in the diagnostic procedure of physicians
and computer-aided diagnosis systems. We propose a two-step deep learning framework for …
and computer-aided diagnosis systems. We propose a two-step deep learning framework for …