[Submitted on 16 Jul 2026]
Title:DINOv3-MIL: Per-Kidney Multi-Label Tumour and Cyst Detection from Foundation-Model Patch Tokens on KiTS23
View a PDF of the paper titled DINOv3-MIL: Per-Kidney Multi-Label Tumour and Cyst Detection from Foundation-Model Patch Tokens on KiTS23, by Vishalakshi M and Sahil Sharma and Pramod Kumar P
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Abstract:Foundation vision models trained on natural images transfer to medical tasks without domain pre-training, but volumetric classification requires aggregating tens of thousands of patch tokens per study, and the aggregator constrains how the resulting model can be interpreted. We compare three aggregators on identical frozen DINOv3 ViT-H/16+ features for renal tumour/cyst detection on KiTS23 (966 kidneys; n=97 test): a CLS-token linear probe, gated attention multiple instance learning (MIL) over 55,296 patch tokens, and a prototype head following ProtoViT. Attention MIL achieves the highest AUROC for tumour (0.74, 95% CI 0.64-0.83) and cyst (0.80, 0.70-0.88), with attention enriched 7.5-9.8x over chance within annotated lesions. The prototype head does not transfer to cyst detection (AUROC 0.51), exposing an interpretability-performance trade-off at this token scale.
Comments: Accepted at MIUA 2026 (poster). To appear in Frontiers in Medical Technology
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.22687 [cs.CV]
(or arXiv:2607.22687v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.22687
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sahil Sharma Dr. [view email] [v1] Thu, 16 Jul 2026 14:50:22 UTC (326 KB)
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