SlimFormer++: Lean 3D Segmentation for CT, MRI and Ultrasound
An nnU-Net v2 trainer that keeps token mixing only where it helps, with released weights and a 3D Slicer extension that runs on CPU.
October 27, 2026
Guides, project breakdowns, and lessons learned from building medical imaging platforms — written by the PYCAD team.
An nnU-Net v2 trainer that keeps token mixing only where it helps, with released weights and a 3D Slicer extension that runs on CPU.
One encoder turns CT and MRI volumes and 2D images from nine modalities into shared tokens, with MIT code and public weights.
A 2D ranker built on chest X-ray eye-tracking picks the CT slices a 2D VLM should check, at a fraction of 3D cost.
A likelihood estimator in an open Julia CT simulator handles dual-kVp, dual-source and photon-counting data and keeps every energy window.
We build custom medical imaging platforms — advanced DICOM viewers, AI segmentation, and the clinical systems around them.
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