Deep learning on histopathological images for colorectal cancer diagnosis: a systematic review
Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …
common in men, with an increasing incidence. Pathology diagnosis complemented with …
Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data
Despite the remarkable advances in cancer diagnosis, treatment, and management over the
past decade, malignant tumors remain a major public health problem. Further progress in …
past decade, malignant tumors remain a major public health problem. Further progress 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 …
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 …
[HTML][HTML] CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us
further understand the relationship between histology and patient outcome. To drive …
further understand the relationship between histology and patient outcome. To drive …
[HTML][HTML] Data-driven color augmentation for H&E stained images in computational pathology
Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs
are high-resolution digitized histopathology images, stained with chemical reagents to …
are high-resolution digitized histopathology images, stained with chemical reagents to …
[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] Annotating for artificial intelligence applications in digital pathology: a practical guide for pathologists and researchers
Training machine learning models for artificial intelligence (AI) applications in pathology
often requires extensive annotation by human experts, but there is little guidance on the …
often requires extensive annotation by human experts, but there is little guidance on the …
A comprehensive survey of intestine histopathological image analysis using machine vision approaches
Y Jing, C Li, T Du, T Jiang, H Sun, J Yang, L Shi… - Computers in Biology …, 2023 - Elsevier
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is
the third most common malignancy and the fourth leading cause of cancer death worldwide …
the third most common malignancy and the fourth leading cause of cancer death worldwide …
A series-based deep learning approach to lung nodule image classification
Simple Summary Medical image classification is an important task in computer-aided
diagnosis, medical image acquisition, and mining. Although deep learning has been shown …
diagnosis, medical image acquisition, and mining. Although deep learning has been shown …