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- Technical University of MunichDE103 papers
- TUM KlinikumDE21 papers
- Ludwig-Maximilians-Universität MünchenDE20 papers
- Imperial College LondonGB13 papers
- Heidelberg UniversityDE11 papers
- University of ZurichCH11 papers
- Harvard UniversityUS10 papers
- King's College LondonGB10 papers
- Centre National de la Recherche ScientifiqueFR8 papers
- ETH ZurichCH8 papers
- Institute of Biological and Medical ImagingDE8 papers
- German Cancer Research CenterDE7 papers
12 papers · 1 filter
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…
Transformer-Based Hematological Malignancy Prediction from Peripheral Blood Smears in a Real-World Cohort
Muhammed Furkan Dasdelen, Ivan Kukuljan, Peter Lienemann +6
Peripheral blood smears remain a cornerstone in the diagnosis of hematological neoplasms, offering rapid and valuable insights that inform subsequent diagnostic steps. However, sin…
MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder
Ayhan Can Erdur, Christian Beischl, Daniel Scholz +4
Missing input sequences are common in medical imaging data, posing a challenge for deep learning models reliant on complete input data. In this work, inspired by MultiMAE [2], we d…
Beyond Quantification: Navigating Uncertainty in Professional AI Systems
Sylvie Delacroix, Diana Robinson, Umang Bhatt +12
The growing integration of large language models across professional domains transforms how experts make critical decisions in healthcare, education, and law. While significant res…
RedDino: A foundation model for red blood cell analysis
Luca Zedda, Andrea Loddo, Cecilia Di Ruberto +1
Red blood cells (RBCs) are essential to human health, and their precise morphological analysis is important for diagnosing hematological disorders. Despite the promise of foundatio…
Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset
A. Piffer, J. A. Buchner, A. G. Gennari +11
Background Brain tumours are the most common solid malignancies in children, encompassing diverse histological, molecular subtypes and imaging features and outcomes. Paediatric bra…