MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
Skin lesion segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease classification. However, dermoscopic images are challenging due to blurred boundaries, low contrast, large shape variations, and artifacts such as hair and shadows. Recently, diffusion models have shown strong performance in medical image segmentation thanks to their progressive denoising and distribution modeling capabilities. Nevertheless, existing
Record details
Published: 25 June 2026
Source: arXiv
Category: Research
Topics: Healthcare
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis
arXiv · 25 June 2026
SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages
arXiv · 25 June 2026
Diagnosing Task Insensitivity in Language Agents
arXiv · 25 June 2026
XMSE-Aware Adaptive Empirical Bayes Estimation
arXiv · 25 June 2026
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
arXiv · 25 June 2026
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
arXiv · 25 June 2026
How to cite this record
ethics.ai (25 June 2026), “MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation,” evidence record 577, https://ethics.ai/record/577 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.