[HTML][HTML] HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

M Van Rijthoven, M Balkenhol, K Siliņa… - Medical image …, 2021 - Elsevier
We propose HookNet, a semantic segmentation model for histopathology whole-slide
images, which combines context and details via multiple branches of encoder-decoder …

Context-aware convolutional neural network for grading of colorectal cancer histology images

M Shaban, R Awan, MM Fraz, A Azam… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
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 …

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 …

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 …

Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review

D Tan, NFM Nasir, HA Manan, N Yahya - Cancer/Radiothérapie, 2023 - Elsevier
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 …

Multiple instance captioning: Learning representations from histopathology textbooks and articles

J Gamper, N Rajpoot - … of the IEEE/CVF conference on …, 2021 - openaccess.thecvf.com
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 …

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 …

A means of assessing deep learning-based detection of ICOS protein expression in colon cancer

MMK Sarker, Y Makhlouf, SG Craig, MP Humphries… - Cancers, 2021 - mdpi.com
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 …

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 …

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 …