Recent applications of artificial intelligence from histopathologic image-based prediction of microsatellite instability in solid cancers: a systematic review
Simple Summary Although the evaluation of microsatellite instability (MSI) is important for
immunotherapy, it is not feasible to test MSI in all cancers due to the additional cost and …
immunotherapy, it is not feasible to test MSI in all cancers due to the additional cost and …
Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre …
Background Response to immunotherapy in gastric cancer is associated with microsatellite
instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We …
instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We …
Deep learning of histopathological features for the prediction of tumour molecular genetics
Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the
individual through developments in immunotherapies and targeted therapies. However, long …
individual through developments in immunotherapies and targeted therapies. However, long …
A federated learning system for histopathology image analysis with an orchestral stain-normalization GAN
Currently, data-driven based machine learning is considered one of the best choices in
clinical pathology analysis, and its success is subject to the sufficiency of digitized slides …
clinical pathology analysis, and its success is subject to the sufficiency of digitized slides …
PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images
J Lou, J Xu, Y Zhang, Y Sun, A Fang, J Liu… - Computer Methods and …, 2022 - Elsevier
Abstract Background and Objective Recent studies have shown that colorectal cancer
(CRC) patients with microsatellite instability high (MSI-H) are more likely to benefit from …
(CRC) patients with microsatellite instability high (MSI-H) are more likely to benefit from …
Identify representative samples by conditional random field of cancer histology images
Pathology analysis is crucial to precise cancer diagnoses and the succeeding treatment
plan as well. To detect abnormality in histopathology images with prevailing patch-based …
plan as well. To detect abnormality in histopathology images with prevailing patch-based …
Mine local homogeneous representation by interaction information clustering with unsupervised learning in histopathology images
Background and objective: The success of data-driven deep learning for histopathology
images often depends on high-quality training sets and fine-grained annotations. However …
images often depends on high-quality training sets and fine-grained annotations. However …
Sampling based tumor recognition in whole-slide histology image with deep learning approaches
Histopathological identification of tumor tissue is one of the routine pathological diagnoses
for pathologists. Recently, computational pathology has been successfully interpreted by a …
for pathologists. Recently, computational pathology has been successfully interpreted by a …
Su-sampling based active learning for large-scale histopathology image
Expensive annotation cost has always been a critical obstacle in deep learning systems, in
particular for the applications requiring domain experts' knowledge, such as medical image …
particular for the applications requiring domain experts' knowledge, such as medical image …
Cluster image patches with multiple mutual information in unlabelled whole-slide image
The massive annotation workload has always hindered the progress towards an automatic
analysis of gigapixel whole-slide images. Histologically, individual patches from a …
analysis of gigapixel whole-slide images. Histologically, individual patches from a …