2 citations · 2 across the 6 of their papers we have counts for
8 papers · 1 filter
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…
Deep learning motion correction of quantitative stress perfusion cardiovascular magnetic resonance
Noortje I. P. Schueler, Nathan C. K. Wong, Richard J. Crawley +3
Background: Quantitative stress perfusion cardiovascular magnetic resonance (CMR) is a powerful tool for assessing myocardial ischemia. Motion correction is essential for accurate…
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…
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…
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…
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…