AIMC Topic: Eosine Yellowish-(YS)
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BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images.
Database : the journal of biological databases and curation
Oct 17, 2022
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer ...
Dual Encoder Attention U-net for Nuclei Segmentation.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
Nuclei segmentation in whole slide images (WSIs) stained with Hematoxylin and Eosin (H&E) dye, is a key step in computational pathology which aims to automate the laborious process of manual counting and segmentation. Nuclei segmentation is a challen...
AI Neuropathologist: an innovative technology enabling a faultless pathological diagnosis?
Neuro-oncology
Jan 30, 2021
Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers.
Neuro-oncology
Jan 30, 2021
BACKGROUND: Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and eosin stained slides by neuropathologists. With advancing art...
Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.
The Lancet. Oncology
Jan 1, 2021
BACKGROUND: Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients...
Hematoxylin
Eosine Yellowish-(YS)
Reproducibility of Results
Colorectal Neoplasms
Diagnosis, Computer-Assisted
Deep Learning
Humans
Phenotype
Microsatellite Instability
Coloring Agents
Microscopy
Image Interpretation, Computer-Assisted
Staining and Labeling
Predictive Value of Tests
Genetic Predisposition to Disease
Deep learning for prediction of colorectal cancer outcome: a discovery and validation study.
Lancet (London, England)
Feb 1, 2020
BACKGROUND: Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal ...
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