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negichi/
MVAA26-jmees-weights

Name: MVAA26-jmees-weights
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MVAA 2026 — Team Jmees final-system weights

Trained checkpoints of our solution to the MICCAI 2026 Mitral Valve Anatomy Analysis (MVAA) challenge (final v026 system; hidden-test DSC 0.838 / 0.887 / 0.836 for CT / TEE / video).

Code: https://github.com/ShunsukeKikuchi/MVAA26-jmees-sol
Paper: Solution for MVAA challenge 2026: Ensemble, Tool-paste Augmentation and Tool-free View Generation (MWM2026)

Layout

Folder	Contents	Networks
`t1/`	Task 1 CT members	plainconv+pseudo (a15), SwinUNETR scratch/BTCV (swin_a09/a10), 2.5D yz / oblique / distortion (a12*), 2D-encoder+3D-decoder hybrid (a13) — 5 folds each
`t2/`	Task 2 TEE members	plainconv (a05) ×5, plainconv+MVSeg-external (a07) ×5
`t3/`	Task 3 video members	UNet++/EfficientNet-B7 (a04), MaxViT-L (a09), ConvNeXt-L DINOv3 (a11) — 5 folds each
`nnunet_results/`	nnU-Net v2 results trees	Dataset001_MitralCT + Dataset002_TEE, `3d_fullres`, 5 folds each

WEIGHTS_MANIFEST.sha256 lists the SHA-256 of every checkpoint: sha256sum -c WEIGHTS_MANIFEST.sha256

Download

bash

pip install -U huggingface_hub
hf download negichi/MVAA26-jmees-weights --local-dir weights/

The resulting weights/ tree is exactly what submission/v026_final_docker/bundle_weights.sh in the code repository expects.

License / intended use

Code and weights are released under MIT. The models were trained on MVAA 2026 challenge data (plus the external corpora documented in the paper) for research purposes; they are not medical devices and must not be used for clinical decision-making.