Tested on 12 OOD tasks across 🧴dermatology, 🩻 chest X-ray, ultrasound & 🔬 histopathology.
💥 DIsoN consistently performs strongly against state-of-the-art methods, with higher AUROC and fewer false positives.

Attention bad pun: 🧹 DIsoN cleans up OOD samples like a Dyson 💨

DIsoN enables comparing a test sample with the training data distribution, without data transfer!

How?
🔑 We train a binary classifier per test sample to “isolate” it from training data.
📈 The more training steps needed → the more likely the sample is in-distribution.

In medical imaging, safe deployment isn’t just about accuracy.

⚠️ Models must flag unusual scans (artifacts, rare conditions) so clinicians can double-check.

But there’s a problem:
📦 Training data is often private, large, and unavailable after deployment.

Whoop #NeurIPS2025 accepted! 🎉
Meet DIsoN, our 🧹💨 privacy-preserving OOD detector that compares test samples to training data without ever sharing the training data.

We make Out-of-Distribution detection decentralized!

📄Paper: arxiv.org/pdf/2506.09024
🧵👇

We propose the FedUniBrain framework: Train a single model across decentralized MRI datasets with:
✔️ Different brain diseases per dataset
✔️ Different modality combinations per dataset
✔️ No data sharing

Traditional brain segmentation models are disease-specific and rely on predefined MRI modalities for both training and inference. They can’t handle other diseases or scans with different input modalities🚫Plus, patient privacy prevents the creation of big centralized databases🧠

🚀Excited to share our latest work: 🧠FedUniBrain Framework, a necessary step towards training foundation models for multimodal MRIs with Federated Learning, accepted at #WACV25 and selected for an oral!

🔗 arXiv: arxiv.org/pdf/2406.11636
💻 GitHub: github.com/FelixWag/Fed...
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