Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
Domain adaptation: challenges, methods, datasets, and applications
Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …
on another set of data (target domain), which is different but has similar properties as the …
Measuring domain shift for deep learning in histopathology
The high capacity of neural networks allows fitting models to data with high precision, but
makes generalization to unseen data a challenge. If a domain shift exists, ie differences in …
makes generalization to unseen data a challenge. If a domain shift exists, ie differences in …
Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations
The digitalization of clinical workflows and the increasing performance of deep learning
algorithms are paving the way towards new methods for tackling cancer diagnosis …
algorithms are paving the way towards new methods for tackling cancer diagnosis …
Current developments of artificial intelligence in digital pathology and its future clinical applications in gastrointestinal cancers
Simple Summary The rapid development of technology has enabled numerous applications
of artificial intelligence (AI), especially in medical science. Histopathological assessment of …
of artificial intelligence (AI), especially in medical science. Histopathological assessment of …
[HTML][HTML] Generative adversarial networks in digital pathology and histopathological image processing: a review
Digital pathology is gaining prominence among the researchers with developments in
advanced imaging modalities and new technologies. Generative adversarial networks …
advanced imaging modalities and new technologies. Generative adversarial networks …
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
R Yamashita, J Long, S Banda, J Shen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Suboptimal generalization of machine learning models on unseen data is a key challenge
which hampers the clinical applicability of such models to medical imaging. Although …
which hampers the clinical applicability of such models to medical imaging. Although …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis
H Xiang, J Shen, Q Yan, M Xu, X Shi, X Zhu - Medical Image Analysis, 2023 - Elsevier
Recently, convolutional neural networks (CNNs) directly using whole slide images (WSIs) for
tumor diagnosis and analysis have attracted considerable attention, because they only …
tumor diagnosis and analysis have attracted considerable attention, because they only …
Weakly supervised joint whole-slide segmentation and classification in prostate cancer
The identification and segmentation of histological regions of interest can provide significant
support to pathologists in their diagnostic tasks. However, segmentation methods are …
support to pathologists in their diagnostic tasks. However, segmentation methods are …