38 citations · 66 across the 9 of their papers we have counts for
10 papers
Feature Space Guidance for Breast Cancer Classification in DCE-MRI
Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss +6
Dynamic contrast enhanced breast MRI (DCE-MRI) is a powerful clinical tool for breast cancer detection, providing high resolution anatomical detail together with rich temporal cont…
nnFoundation: 3D Foundation Models for Radiology
Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski +80
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise…
Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation
Jonathan Suprijadi, Raphael Stock, Moritz Langenberg +10
Vision-language models offer a promising path toward automating radiology report generation, but applying them to full 3D CT volumes poses substantial computational challenges. Mod…
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Yudi Sang, Yanzhen Liu, Sutuke Yibulayimu +33
The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and effici…
Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation
Karol Gotkowski, Klaus H. Maier-Hein, Fabian Isensee
Scribble supervision has emerged as a promising approach for reducing annotation costs in medical 3D segmentation by leveraging sparse annotations instead of voxel-wise labels. Whi…
ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling
Karol Gotkowski, Shuvam Gupta, Jose R. A. Godinho +3
Minerals, metals, and plastics are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing ore extraction and recuperation from r…
Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Moritz Fuchs +5
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high res…
Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation
Camila Gonzalez, Karol Gotkowski, Andreas Bucher +3
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models a…
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı +39
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms,…
M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning
Karol Gotkowski, Camila Gonzalez, Andreas Bucher +1
M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans. The attention maps can b…