TeF-SAM: Prototype memory for medical lesion segmentation with text-free inference.

Medical lesion segmentation often depends on large, high-quality pixel-level annotations, which are costly to obtain because they require expert delineation. Recent multimodal methods use clinical text to improve visual representation learning, but they usually require paired image-report data during training and text input during inference. This dependency limits their use when paired reports are scarce or when segmentation is performed before report generation. To address this limitation, we propose TeF-SAM, a Segment Anything Model (SAM)-based framework for limited-text training and text-free medical lesion segmentation. TeF-SAM builds a Persistent Region-Informed Semantic Memory (PRISM) from the paired reports available during training. PRISM distils segmentation-relevant textual knowledge into a region-aware prototype space, allowing image features to retrieve semantic cues at inference without online text input. We further introduce Prototype-Driven Regional Lesion Detection (PRLD), which combines prototype-level semantic responses with multi-scale visual features to generate SAM-compatible prompts, including coarse masks, points, and semantic embeddings. We evaluate TeF-SAM on three lesion segmentation benchmarks spanning different anatomies and imaging modalities: QaTa-COV19, MosMedData+, and BUSI. Experimental results show that TeF-SAM achieves competitive segmentation performance using only 1%-15% of the available paired reports and requires no text input during inference. Our code will be released at: https://github.com/Jinxinya2/TeF-SAM.