2 citations · 2 across the 4 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…
On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation
Ruben T. Lucassen, Tijn van de Luijtgaarden, Sander P. J. Moonemans +3
Vision-language models in pathology enable multimodal case retrieval and automated report generation. Many of the models developed so far, however, have been trained on pathology r…
PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting
Hong Liu, Haosen Yang, Evi M. C. Huijben +4
Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained image-mask pairs annotated by pathologists. Thus, synthesizing histopathology data…
Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions
Ruben T. Lucassen, Sander P. J. Moonemans, Tijn van de Luijtgaarden +3
Millions of melanocytic skin lesions are examined by pathologists each year, the majority of which concern common nevi (i.e., ordinary moles). While most of these lesions can be di…
Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions
Ruben T. Lucassen, Nikolas Stathonikos, Gerben E. Breimer +2
Pathologists are facing an increasing workload due to a growing volume of cases and the need for more comprehensive diagnoses. Aiming to facilitate workload reduction and faster tu…
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…