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20242026
most citedBeyond accuracy: quantifying the reliability of Multiple Instance Learning for Whole Slide Image classification

2 citations · 2 across the 6 of their papers we have counts for

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eess.IV2025

Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI

Marijn Borghouts, Richard McKinley, Manuel Köstner +3

Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge…

eess.IV2025

DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography

Marcus J. Vroemen, Yuqian Chen, Yui Lo +5

Diffusion MRI (dMRI) tractography enables in vivo mapping of brain structural connections, but traditional connectome generation is time-consuming and requires gray matter parcella…

eess.IV2025

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…

eess.IV2025

Adaptive Prototype Learning for Multimodal Cancer Survival Analysis

Hong Liu, Haosen Yang, Federica Eduati +2

Leveraging multimodal data, particularly the integration of whole-slide histology images (WSIs) and transcriptomic profiles, holds great promise for improving cancer survival predi…

eess.IV2024

Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles

Evi M. C. Huijben, Sina Amirrajab, Josien P. W. Pluim

Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems, as abnormal patterns in images could hamper their performance. However…