23 citations · 62 across the 16 of their papers we have counts for
17 papers
Genetically Aligned Patient Representations Improve Hematological Diagnosis
Muhammed Furkan Dasdelen, Fatih Ozlugedik, Ilaria Looser +3
Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream diagnostic tasks. Hematologic…
A Multicenter Benchmark of Multiple Instance Learning Models for Lymphoma Subtyping from HE-stained Whole Slide Images
Rao Muhammad Umer, Daniel Sens, Jonathan Noll +12
Timely and accurate lymphoma diagnosis is essential for guiding cancer treatment. Standard diagnostic practice combines hematoxylin and eosin (HE)-stained whole slide images with i…
Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings
Christian Grashei, Christian Brechenmacher, Rao Muhammad Umer +4
Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exceed a billion parameters and prod…
CytoDiff: AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Jan Carreras Boada, Rao Muhammad Umer, Carsten Marr
Biomedical datasets are often constrained by stringent privacy requirements and frequently suffer from severe class imbalance. These two aspects hinder the development of accurate…
DomainLab: A modular Python package for domain generalization in deep learning
Xudong Sun, Carla Feistner, Alexej Gossmann +10
Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniq…
Federated Learning for Data and Model Heterogeneity in Medical Imaging
Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti
Federated Learning (FL) is an evolving machine learning method in which multiple clients participate in collaborative learning without sharing their data with each other and the ce…