5 citations · 7 across the 4 of their papers we have counts for
4 papers
Attention-Based Multimodal Survival Prediction with Cross-Modal Bilinear Fusion
Hassan Keshvarikhojasteh, Josien P. W. Pluim, Mitko Veta
We propose a novel multimodal deep learning framework for patient-level survival prediction, which integrates whole-slide histology features, RNA-seq expression profiles, and clini…
A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Hassan Keshvarikhojasteh, Mihail Tifrea, Sibylle Hess +2
Multiple instance learning (MIL) is a promising approach for weakly supervised classification in pathology using whole slide images (WSIs). However, conventional MIL methods such a…
Beyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification
Hassan Keshvarikhojasteh, Marc Aubreville, Christof A. Bertram +2
Machine learning models have become integral to many fields, but their reliability, defined as producing dependable, trustworthy, and domain-consistent predictions, remains a criti…
Multi-head Attention-based Deep Multiple Instance Learning
Hassan Keshvarikhojasteh, Josien Pluim, Mitko Veta
This paper introduces MAD-MIL, a Multi-head Attention-based Deep Multiple Instance Learning model, designed for weakly supervised Whole Slide Images (WSIs) classification in digita…