Article

Accurate spatial quantification in computational pathology with multiple instance learning

2024-04-26

Abstract excerpt

Spatial quantification is a critical step in most computational pathology tasks, from guiding pathologists to areas of clinical interest to discovering tissue phenotypes behind novel biomarkers. To circumvent the need for manual annotations, modern computational pathology methods have favoured multiple-instance learning approaches that can accurately predict whole-slide image labels, albeit at the expense of losin...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ae211f2b-918d-5ace-afa6-acbfe7d6480f
DOI: 10.1101/2024.04.25.24306364

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Accurate spatial quantification in computational pathology with multiple instance learningDOI 10.1101/2024.04.25.24306364Read

Multiple instance learning on tile level-pathology images provides accurate and interpretable classification for breast cancer molecular subtypesSemantic proximity Read

Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep LearningSemantic proximity Read

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.Semantic proximity Read

Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging DataSemantic proximity Read

HipoMap: Slide Representation Map for Whole Slide-based Histopathological Image AnalysisSemantic proximity Read

Inferring single-cell spatial gene expression with tissue morphology via explainable deep learningSemantic proximity Read

Multi-diagnosis multi-instance learning for auxiliary gene mutation diagnosis in whole slide images.Semantic proximity Read

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastomaSemantic proximity Read

Classpose drives the discovery of colorectal cancer phenotypes in clinical grade whole slide imagesSemantic proximity Read

PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation modelSemantic proximity Read

Prediction of microsatellite instability in colorectal cancer based on tissue phenotypes inferred from pathological whole slide images using self-distillation.Semantic proximity Read

HistoMIL: a Python package for training Multiple Instance Learning models on histopathology slidesSemantic proximity Read