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Found 5 papers, 1 papers with code
Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations
The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images
To train a robust deep learning model, one usually needs a balanced set of categories in the training data.
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
Ranked #2 on
Multi-tissue Nucleus Segmentation
on CoNSeP
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures.
Ranked #4 on
Colorectal Gland Segmentation:
on CRAG
Novel digital tissue phenotypic signatures of distant metastasis in colorectal cancer
Distant metastasis is the major cause of death in colorectal cancer (CRC).
