480 citations · 487 across the 4 of their papers we have counts for
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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…
WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images
Hong Liu, Haosen Yang, Paul J. van Diest +2
The Segment Anything Model (SAM) marks a significant advancement in segmentation models, offering robust zero-shot abilities and dynamic prompting. However, existing medical SAMs a…
Multiple Instance Learning with random sampling for Whole Slide Image Classification
H. Keshvarikhojasteh, J. P. W. Pluim, M. Veta
In computational pathology, random sampling of patches during training of Multiple Instance Learning (MIL) methods is computationally efficient and serves as a regularization strat…
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Mitko Veta, Paul J. van Diest, Stefan M. Willems +26
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be on…