TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models

• URL: http://arxiv.org/abs/2607.09562v1
• Date: Fri, 10 Jul 2026 16:06:39 GMT
• Title: TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models
• Abstract summary: Medical Vision-Language Models (VLMs) exhibit strong zero-shot performance, yet their effectiveness still declines on out-of-distribution (OOD) data.<n>We present TCLA, a purely training-free few-shot adaptation method for Medical VLMs, which is fast and model-agnostic.
• Score: 6.098258627039598
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Medical Vision-Language Models (VLMs) exhibit strong zero-shot performance, yet their effectiveness still declines on out-of-distribution (OOD) data due to domain shifts and class bias inherited from large-scale pretraining. Existing few-shot adaptation methods typically introduce additional trainable components, which can be unstable in extremely low-data regimes (e.g., 1-shot), and lack robustness on different medical data. We present TCLA, a purely training-free few-shot adaptation method for Medical VLMs, which is fast and model-agnostic. TCLA corrects inference logits based on a small set of support samples, boosting pretrained VLMs performance by improving inter-class deconfusion and reducing domain shift. Extensive experiments on nine datasets across multiple medical imaging modalities including X-ray, Ultrasound, MRI, CT, Histopathology, demonstrate that TCLA consistently improves OOD performance of Medical VLMs and, in most of cases, outperforms existing training-based adaptation methods.

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