DoubleTake: Contrastive Reasoning for Faithful Decision-Making in Medical Imaging
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866915770259210240 |
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| author | Patel, Daivik Patel, Shrenik |
| author_facet | Patel, Daivik Patel, Shrenik |
| contents | Accurate decision making in medical imaging requires reasoning over subtle visual differences between confusable conditions, yet most existing approaches rely on nearest neighbor retrieval that returns redundant evidence and reinforces a single hypothesis. We introduce a contrastive, document-aware reference selection framework that constructs compact evidence sets optimized for discrimination rather than similarity by explicitly balancing visual relevance, embedding diversity, and source-level provenance using ROCO embeddings and metadata. While ROCO provides large-scale image-caption pairs, it does not specify how references should be selected for contrastive reasoning, and naive retrieval frequently yields near-duplicate figures from the same document. To address this gap, we release a reproducible reference selection protocol and curated reference bank that enable a systematic study of contrastive retrieval in medical image reasoning. Building on these contrastive evidence sets, we propose Counterfactual-Contrastive Inference, a confidence-aware reasoning framework that performs structured pairwise visual comparisons and aggregates evidence using margin-based decision rules with faithful abstention. On the MediConfusion benchmark, our approach achieves state-of-the-art performance, improving set-level accuracy by nearly 15% relative to prior methods while reducing confusion and improving individual accuracy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_02894 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DoubleTake: Contrastive Reasoning for Faithful Decision-Making in Medical Imaging Patel, Daivik Patel, Shrenik Computer Vision and Pattern Recognition Machine Learning Accurate decision making in medical imaging requires reasoning over subtle visual differences between confusable conditions, yet most existing approaches rely on nearest neighbor retrieval that returns redundant evidence and reinforces a single hypothesis. We introduce a contrastive, document-aware reference selection framework that constructs compact evidence sets optimized for discrimination rather than similarity by explicitly balancing visual relevance, embedding diversity, and source-level provenance using ROCO embeddings and metadata. While ROCO provides large-scale image-caption pairs, it does not specify how references should be selected for contrastive reasoning, and naive retrieval frequently yields near-duplicate figures from the same document. To address this gap, we release a reproducible reference selection protocol and curated reference bank that enable a systematic study of contrastive retrieval in medical image reasoning. Building on these contrastive evidence sets, we propose Counterfactual-Contrastive Inference, a confidence-aware reasoning framework that performs structured pairwise visual comparisons and aggregates evidence using margin-based decision rules with faithful abstention. On the MediConfusion benchmark, our approach achieves state-of-the-art performance, improving set-level accuracy by nearly 15% relative to prior methods while reducing confusion and improving individual accuracy. |
| title | DoubleTake: Contrastive Reasoning for Faithful Decision-Making in Medical Imaging |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2602.02894 |