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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
24 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…
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…
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…
cp_measure: API-first feature extraction for image-based profiling workflows
Alán F. Muñoz, Tim Treis, Alexandr A. Kalinin +4
Biological image analysis has traditionally focused on measuring specific visual properties of interest for cells or other entities. A complementary paradigm gaining increasing tra…
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
Nicholas Konz, Richard Osuala, Preeti Verma +16
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, t…
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Reuben Dorent, Roya Khajavi, Tagwa Idris +24
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentati…