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arXiv AI By Muhammad Muhtasim Shahriar, M. F. Mridha

LENS-GRF: Permutation-Invariant Lesion Evidence Network with Gated Residual Fusion for Acne Severity Grading and Multi-Rater Clinical Oracle Analysis

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LENS‑GRF is a permutation‑invariant lesion evidence network that uses a Set‑Transformer and gated residual fusion to combine global facial context with localized lesion patches for four‑class acne severity grading. The framework integrates adaptive facial skin segmentation, a Vision Transformer prior, and a lesion set transformer that encodes spatial geometry, with a gating mechanism that modulates local residual contributions. In experiments on ACNE04 and PLSBRACNE01, the fully automated model achieved 80.82% accuracy, while using ground‑truth lesion annotations raised accuracy to 95.89% and a Quadratic Weighted Kappa of 0.9753; zero‑shot evaluation on the full cohort yielded 35.00% accuracy versus 42.50% for a global baseline, and oracle analyses on a 148‑subject cohort showed improved accuracy and QWK up to 47.97% and 0.5799.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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