1.2k citations · 1.9k across the 32 of their papers we have counts for
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Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit +9
Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is…
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Fernando Navarro, Christopher Watanabe, Suprosanna Shit +4
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pre…
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
Fabian Balsiger, Alain Jungo, Naren Akash R J +12
The MICCAI conference has encountered tremendous growth over the last years in terms of the size of the community, as well as the number of contributions and their technical succes…
Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning
Christina Bukas, Bailiang Jian, Luis F. Rodriguez Venegas +9
Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore t…
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
Hans Liebl, David Schinz, Anjany Sekuboyina +13
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning…
A Relational-learning Perspective to Multi-label Chest X-ray Classification
Anjany Sekuboyina, Daniel Oñoro-Rubio, Jens Kleesiek +1
Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i.e. learning to map an image directly to its binary labels. Such approach…