Mycosis fungoides is characterized by widespread skin patches that may progress to plaques and tumors, necessitating precise tumor burden assessment for staging and treatment guidance. However, existing methods, including the widely accepted modified Severity Weighted Assessment Tool (mSWAT), present significant challenges in routine practice owing to their time-consuming nature and interobserver variability. This study developed an artificial intelligence model, mSWAT-Net, to estimate mSWAT scores using clinical images of patients with mycosis fungoides. Notably, the overlap area segmentation submodule of mSWAT-Net addressed double-counting errors in multiangle photos through training on 3904 annotated images generated from 61 three-dimensional human images. Across 2463 standardized full-body photographs from 134 imaging series, mSWAT-Net demonstrated performance comparable with that of experienced cutaneous lymphoma specialists, achieving intraclass correlation coefficients of 0.917 (internal validation) and 0.846 (temporal validation) for mSWAT score. Moreover, mSWAT-Net outperformed 3 junior dermatologists in image-based scoring (intraclass correlation coefficient = 0.917 vs 0.777) and demonstrated robust performance when compared with ground truth derived from 3-dimensional patient imaging (intraclass correlation coefficient = 0.812). Finally, mSWAT-Net was deployed as a free web application to support mycosis fungoides management in clinical settings. These findings highlight the potential of mSWAT-Net as an accurate, automated clinical tool for facilitating patient follow-up, treatment monitoring, and remote consultations.

Image Quality Assessment (IQA) is a fundamental problem in image processing. It is a common principle that human vision is hierarchical: we first perceive global structural information such as contours then focus on local regional details if necessary. Following this principle, we propose a novel framework for IQA by quantifying the degenerations of structural information and region content separately, and mapping both to obtain the objective score. The structural information can be obtained as contours by contour detection techniques. Experiments are conducted to demonstrate its performance in comparison with multiple state-of-the-art methods on two large scale datasets.