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Found 9 papers, 6 papers with code
An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer
Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer.
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.
SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts
Tissue image generation based on bespoke cellular layouts through the proposed framework allows users to generate different histological patterns from arbitrary topological arrangement of different types of cells.
TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge
The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model.
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.
All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?"
ALBRT: Cellular Composition Prediction in Routine Histology Images
Cellular composition prediction, i. e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc.
A Generalized Meta-loss function for regression and classification using privileged information
Learning using privileged information (LUPI) is a powerful heterogenous feature space machine learning framework that allows a machine learning model to learn from highly informative or privileged features which are available during training only to generate test predictions using input space features which are available both during training and testing.
