538 citations · 1.8k across the 28 of their papers we have counts for
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BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation
Luc Bouteille, Frederic Jonske, Jens Kleesiek +1
Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically critical lesions disproportion…
Region-Normalized DPO for Medical Image Segmentation under Noisy Judges
Hamza Kalisch, Constantin Seibold, Jens Kleesiek +2
While dense pixel-wise annotations remain the gold standard for medical image segmentation, they are costly to obtain and limit scalability. In contrast, many deployed systems alre…
Automatic Fine-grained Segmentation-assisted Report Generation
Frederic Jonske, Constantin Seibold, Osman Alperen Koras +6
Reliable end-to-end clinical report generation has been a longstanding goal of medical ML research. The end goal for this process is to alleviate radiologists' workloads and provid…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
Frederic Jonske, Moon Kim, Enrico Nasca +8
It is an open secret that ImageNet is treated as the panacea of pretraining. Particularly in medical machine learning, models not trained from scratch are often finetuned based on…