[HTML][HTML] HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
We propose HookNet, a semantic segmentation model for histopathology whole-slide
images, which combines context and details via multiple branches of encoder-decoder …
images, which combines context and details via multiple branches of encoder-decoder …
Context-aware convolutional neural network for grading of colorectal cancer histology images
Digital histology images are amenable to the application of convolutional neural networks
(CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally …
(CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally …
Molecular pathology of lung cancer
JJ Saller, TA Boyle - Cold Spring Harbor …, 2021 - perspectivesinmedicine.cshlp.org
This overview of the molecular pathology of lung cancer includes a review of the most
salient molecular alterations of the genome, transcriptome, and the epigenome. The insights …
salient molecular alterations of the genome, transcriptome, and the epigenome. The insights …
Adaptive weighting multi-field-of-view CNN for semantic segmentation in pathology
H Tokunaga, Y Teramoto… - Proceedings of the …, 2019 - openaccess.thecvf.com
Automated digital histopathology image segmentation is an important task to help
pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer …
pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer …
Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review
Purpose This study aims to perform a comprehensive systematic review of deep learning
(DL) models in predicting RT-induced toxicity. Materials and methods A literature review was …
(DL) models in predicting RT-induced toxicity. Materials and methods A literature review was …
Multiple instance captioning: Learning representations from histopathology textbooks and articles
We present ARCH, a computational pathology (CP) multiple instance captioning dataset to
facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH …
facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH …
Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis
SC Kosaraju, J Hao, HM Koh, M Kang - Methods, 2020 - Elsevier
Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-
aided tissue examination using machine learning techniques, especially convolutional …
aided tissue examination using machine learning techniques, especially convolutional …
A means of assessing deep learning-based detection of ICOS protein expression in colon cancer
Simple Summary In this study, we propose a general artificial intelligence (AI) based
workflow for applying deep learning to the problem of cell identification in …
workflow for applying deep learning to the problem of cell identification in …
Negative pseudo labeling using class proportion for semantic segmentation in pathology
H Tokunaga, BK Iwana, Y Teramoto… - Computer Vision–ECCV …, 2020 - Springer
In pathological diagnosis, since the proportion of the adenocarcinoma subtypes is related to
the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for …
the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for …
Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning
Y Xu, L Jiang, W Chen, S Huang, Z Liu… - Journal of Cancer …, 2023 - Springer
Purpose Rapid diagnosis and risk stratification can provide timely treatment for colorectal
cancer (CRC) patients. Deep learning (DL) is not only used to identify tumor regions in …
cancer (CRC) patients. Deep learning (DL) is not only used to identify tumor regions in …