A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
[HTML][HTML] Translational AI and deep learning in diagnostic pathology
A Serag, A Ion-Margineanu, H Qureshi… - Frontiers in …, 2019 - frontiersin.org
There has been an exponential growth in the application of AI in health and in pathology.
This is resulting in the innovation of deep learning technologies that are specifically aimed at …
This is resulting in the innovation of deep learning technologies that are specifically aimed at …
Lung Infection Segmentation for COVID‐19 Pneumonia Based on a Cascade Convolutional Network from CT Images
R Ranjbarzadeh… - BioMed Research …, 2021 - Wiley Online Library
The COVID‐19 pandemic is a global, national, and local public health concern which has
caused a significant outbreak in all countries and regions for both males and females …
caused a significant outbreak in all countries and regions for both males and females …
Seeing beyond the patch: Scale-adaptive semantic segmentation of high-resolution remote sensing imagery based on reinforcement learning
In remote sensing imagery analysis, patch-based methods have limitations in capturing
information beyond the sliding window. This shortcoming poses a significant challenge in …
information beyond the sliding window. This shortcoming poses a significant challenge in …
Differentiable zooming for multiple instance learning on whole-slide images
Abstract Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …
Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
H Pinckaers, B Van Ginneken… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Due to memory constraints on current hardware, most convolution neural networks (CNN)
are trained on sub-megapixel images. For example, most popular datasets in computer …
are trained on sub-megapixel images. For example, most popular datasets in computer …
[HTML][HTML] Utilizing automated breast cancer detection to identify spatial distributions of tumor-infiltrating lymphocytes in invasive breast cancer
Quantitative assessment of spatial relations between tumor and tumor-infiltrating
lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of …
lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of …
[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 …
[HTML][HTML] A self-supervised contrastive learning approach for whole slide image representation in digital pathology
Image analysis in digital pathology has proven to be one of the most challenging fields in
medical imaging for AI-driven classification and search tasks. Due to their gigapixel …
medical imaging for AI-driven classification and search tasks. Due to their gigapixel …
A state-of-the-art survey of artificial neural networks for whole-slide image analysis: from popular convolutional neural networks to potential visual transformers
In recent years, with the advancement of computer-aided diagnosis (CAD) technology and
whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the …
whole slide image (WSI), histopathological WSI has gradually played a crucial aspect in the …