AIMC Topic: Hematoxylin
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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.
PLoS medicine
Jan 24, 2019
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers....
MuDeRN: Multi-category classification of breast histopathological image using deep residual networks.
Artificial intelligence in medicine
Apr 26, 2018
MOTIVATION: Identifying carcinoma subtype can help to select appropriate treatment options and determining the subtype of benign lesions can be beneficial to estimate the patients' risk of developing cancer in the future. Pathologists' assessment of ...
Efficient deep learning model for mitosis detection using breast histopathology images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 16, 2017
Mitosis detection is one of the critical factors of cancer prognosis, carrying significant diagnostic information required for breast cancer grading. It provides vital clues to estimate the aggressiveness and the proliferation rate of the tumour. The...
Metastasis detection from whole slide images using local features and random forests.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Apr 20, 2017
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in cos...
A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Feb 13, 2017
The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distingu...
Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes.
Pathology, research and practice
Jun 1, 2025
CONTEXT: Staging cancer patients is crucial and requires analyzing all removed lymph nodes microscopically for metastasis. For this pivotal task, convolutional neural networks (CNN) can reduce workload and improve diagnostic accuracy.
A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.
Journal of neuropathology and experimental neurology
Feb 1, 2025
Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In t...
High-rate emphasized DeepLabV3Plus for Semantic Segmentation of Breast Cancer-related Hematoxylin and Eosin-stained Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Deep learning algorithms have been successfully adopted to extract meaningful information from digital images, yet many of them have been untapped in the semantic image segmentation of histopathology images. In this paper, we propose a deep convoluti...
Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group.
Clinical cancer research : an official journal of the American Association for Cancer Research
Jan 17, 2023
PURPOSE: Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS, which strongly correlated with survival; however, predicting these ...
Comment on "Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology: a systematic review".
Minerva urology and nephrology
Dec 1, 2022
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