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Found 6 papers, 1 papers with code
A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia
To address this, we developed a novel artificial intelligence algorithm that can assign an Oral Malignant Transformation (OMT) risk score, based on histological patterns in the in Haematoxylin and Eosin stained whole slide images, to quantify the risk of OED progression.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Mimicking a Pathologist: Dual Attention Model for Scoring of Gigapixel Histology Images
The second component is a hard attention classification model, which further extracts a sequence of multi-resolution glimpses from each tile for classification.
Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images
The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Cellular Segmentation and Composition in Routine Histology Images using Deep Learning
For the prediction of cellular composition with ALBRT on the preliminary test set, we achieved an overall $R^2$ score of 0. 53, consisting of 0. 84 for lymphocytes, 0. 70 for epithelial cells, 0. 70 for plasma and . 060 for eosinophils.
