arXiv cs.AI· Hongyu Cao, Guangfu Guo, Jinghan Zhang, Dakshak Keerthi Chandra, Ying Zhang, Kunpeng Liu·· 12 小时前AI 评分24
GENIQ：用文本到图像生成器自身生成的图像查询皮肤病变分割
Segment What It Can Draw: Querying a Text-to-Image Generator with Its Own Images for Skin Lesion Segmentation
AI 导读
GENIQ 是一种无需训练的方法，利用文本到图像扩散 Transformer 自身生成的健康皮肤与病变图像构建原型方向，再用未标注皮肤镜图像中最像皮肤和病变的图块精炼，从而在无标注掩码条件下完成皮肤病变分割。该方法在三个皮肤镜测试集上取得 0.85-0.88 的 Dice 分数，全部优于零样本与基于词注意力方法，并在其中两个集上的 Dice 和 IoU 超过手工设计的皮肤镜流程。
正文 · 原文
Abstract:Skin lesion segmentation without annotated masks requires the target to be specified through other cues. Existing approaches rely on dermoscopy-specific rules, annotation-derived model priors, or user-selected regions. A general text-to-image diffusion transformer offers a text interface to the target, but its concept-word attention is unreliable on dermoscopy images: in the model's attention-output space, the word-derived skin-to-lesion direction is poorly aligned with the direction separating real skin from real lesions. We show that the direction derived from generated healthy-skin and lesion images is substantially better aligned. Based on this finding, we propose GENIQ, a training-free method that constructs initial skin and lesion prototypes from generated images and refines them using the most skin-like and lesion-like patches from unlabelled dermoscopy images. It scores test patches along the refined direction and applies a gated threshold to suppress contextual spill-over around small lesions. Without inference-time annotations or additional models, GENIQ achieves Dice scores of 0.85-0.88 on three dermoscopy test sets. It outperforms zero-shot and word-based attention methods on all three sets, and a hand-designed dermoscopy pipeline in Dice and IoU on two.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| ACM classes: | I.4.6; I.2.10 |
| Cite as: | arXiv:2611.07061 [cs.CV] |
| (or arXiv:2611.07061v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07061 arXiv-issued DOI via DataCite |
Submission history
From: Hongyu Cao [view email]
[v1]
Wed, 4 Nov 2026 19:20:08 UTC (643 KB)
来源：arXiv cs.AI · arxiv.org