480 citations · 488 across the 7 of their papers we have counts for
7 papers
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…
Histogram- and Diffusion-Based Medical Out-of-Distribution Detection
Evi M. C. Huijben, Sina Amirrajab, Josien P. W. Pluim
Out-of-distribution (OOD) detection is crucial for the safety and reliability of artificial intelligence algorithms, especially in the medical domain. In the context of the Medical…
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
Sina Amirrajab, Yasmina Al Khalil, Cristian Lorenz +3
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks. However,…
Generalized Probabilistic U-Net for medical image segementation
Ishaan Bhat, Josien P. W. Pluim, Hugo J. Kuijf
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution t…