Back to search

Article

MedSAM and Generative AI for segmenting gleason pattern in digital pathology slides of prostate cancer

2025-06-20

Abstract excerpt

Prostate cancer is a leading cause of cancer-related mortality among men, and precise grading of histopathology slides is critical for treatment planning. We introduce a prompt-guided adaptation of the Segment Anything Model (MedSAM) for pixel-level, multi-class Gleason pattern segmentation in haematoxylin–eosin-stained tissue micro-arrays. Using stratified train/validation/test splits of the MICCAI-2019 dataset,...

Topics

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

Identifiers and source

Literature Corpus work
c6653b45-43e5-5f5c-9cc4-88799fbf6c44
DOI
10.22541/au.175039400.09206911/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
MedSAM and Generative AI for segmenting gleason pattern in digital pathology slides of prostate cancerDOI 10.22541/au.175039400.09206911/v1
Synthetic Histology Images for Training AI Models: A Novel Approach to Improve Prostate Cancer DiagnosisSemantic proximity
Coreset Selection for Scalable and Efficient Prostate Cancer Grading in Digital PathologySemantic proximity
A Generalised Vision Transformer-Based Self-Supervised Model for Diagnosing and Grading Prostate Cancer Using Histological ImagesSemantic proximity
Image-based patch selection for deep learning to improve automated Gleason grading in histopathological slidesSemantic proximity
Robust, credible, and interpretable AI-based histopathological prostate cancer gradingSemantic proximity
SortIT — A Tool For Assessing Observer Variability And Creating Ground Truth Image Classification DatasetsSemantic proximity
PathProfiler: Automated Quality Assessment of Retrospective Histopathology Whole-Slide Image Cohorts by Artificial Intelligence – A Case Study for Prostate Cancer ResearchSemantic proximity
A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological imagesSemantic proximity
An Automated, Pathologist-free Gleason Grade Stratifies Disease-free Interval Comparably to Expert Grading from a Single Out-of-distribution SlideSemantic proximity
Explainable AI for CNN-based Prostate Tumor Segmentation in Multi-parametric MRI Correlated to Whole Mount HistopathologySemantic proximity
Prostate cancer risk stratification via non-destructive 3D pathology with annotation-free gland segmentation and analysisSemantic proximity
Cracks in the Foundation: How Data-Hungry and Sensitive to Domain Shift are Vision Foundation Models for Computational Pathology?Semantic proximity
Select a neighboring publication to make it the new centre.