Newest papers

Expandable Modality Experts for Medical Vision-Language Learning

DM BAPPY, Eunsu Kim, Hanjoo Lee, Lee Sh, Sangjeong Ahn · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29881 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

通过扩展的医学成像模式,我们提出了一种医学视觉语言技术,可以围绕一种可扩展的模式专家组建。 其多途转换器将共享的视觉计算与定义上路由的传输向前专家和单独的文本路径相结合,同时保持现有代表性的新领域。 一个模型意识的视觉代码器为在七种模型上构建的可扩展的视觉语言技术提供了面向,以便在1940万个无对称的图像行上进行预先验证。 对比性调节然后使用32300万个图像和文本对比。 额外的基金和光学凝聚力分析专家从对应的数据中学习,而原始图像和文本嵌入路径仍然被冻结。 使用Vit-

Machine translation (Meta M2M100); the English original follows.

Abstract

Learning across medical imaging modalities requires making use of images without paired text and accommodating new domains while retaining existing representations. We propose a medical vision-language technique built around an expandable bank of modality experts. Its Multiway Transformer combines shared visual computation with deterministically routed feed-forward experts and a separate text pathway. A modality-aware visual tokenizer supplies targets for masked pretraining on 19.4 million unpaired image rows across seven modalities; contrastive alignment then uses 3.23 million image-text pairs. Additional fundus and optical coherence tomography experts learn from paired data while the original image and text embedding paths remain frozen. With a ViT-Large backbone, the proposed technique achieves the best or tied-best result in 24 of 25 benchmark settings across nine modalities: 10 of 11 zero-shot classification benchmarks, all 10 linear probes, and all four retrieval benchmarks. Mean balanced accuracy is 0.674 for zero-shot classification and 0.856 for linear probing, and mean bidirectional retrieval recall is 20.70%. A separate three-seed sequential experiment with matched adaptation data yields fundus and OCT retrieval gains of 14.95 and 9.31 percentage points, with no measured loss on preceding fundus or pathology tasks. The preservation argument requires unchanged existing computation paths and inference conditions, and feature-identity checks verify retention on evaluated inputs. The proposed technique provides a modular approach to learning and expanding medical vision-language representations.

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Topics: Computer science, Artificial intelligence, Medical imaging, Modular design, Modality (human–computer interaction), Encoder

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