Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin

arXiv:2405.00908v1 Announce Type: cross
Abstract: Background and Purpose: Identifying the thromboembolism source in ischemic stroke is crucial for treatment and secondary prevention yet is often undetermined. This study describes a self-supervised deep learning approach in digital pathology of emboli for classifying ischemic stroke clot origin from histopathological images. Methods: The dataset included whole slide images (WSI) from the STRIP AI Kaggle challenge, consisting of retrieved clots from ischemic stroke patients following mechanical thrombectomy. Transformer-based deep learning models were developed using …

abstract arxiv classification cs.ai cs.cv cs.lg deep learning digital digital pathology images pathology prevention self-supervised learning stroke study supervised learning transformer treatment type