# Improving Acne Image Grading with Label Distribution Smoothing

arXiv: https://arxiv.org/abs/2403.00268 · HTML: https://mlsanity.com/paper/2403.00268/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)

## Built on by (most cited first, 4 of 4)

- [SkinAgent AI: A Safety-Grounded Multimodal Agentic Framework for Non-Diagnostic Skincare Support](https://mlsanity.com/paper/2609.29341/) (2026)
- [CG-HAF: An Interpretable Global-Local Lesion-Burden Fusion Framework for Ordinal Acne Severity Grading in Agentic Skincare Support](https://mlsanity.com/paper/2609.31326/) (2026)
- [LENS-GRF: Permutation-Invariant Lesion Evidence Network with Gated Residual Fusion for Acne Severity Grading and Multi-Rater Clinical Oracle Analysis](https://mlsanity.com/paper/2610.00294/) (2026)
- [Quantifying Annotation Agreement in Object Detection with Optimal Transport](https://mlsanity.com/paper/2610.19912/) (2026)

## Abstract (arXiv)

Acne, a prevalent skin condition, necessitates precise severity assessment for effective treatment. Acne severity grading typically involves lesion counting and global assessment. However, manual grading suffers from variability and inefficiency, highlighting the need for automated tools. Recently, label distribution learning (LDL) was proposed as an effective framework for acne image grading, but its effectiveness is hindered by severity scales that assign varying numbers of lesions to different severity grades. Addressing these limitations, we proposed to incorporate severity scale information into lesion counting by combining LDL with label smoothing, and to decouple if from global assessment. A novel weighting scheme in our approach adjusts the degree of label smoothing based on the severity grading scale. This method helped to effectively manage label uncertainty without compromising class distinctiveness. Applied to the benchmark ACNE04 dataset, our model demonstrated improved performance in automated acne grading, showcasing its potential in enhancing acne diagnostics. The source code is publicly available at http://github.com/openface-io/acne-lds.