Improving Acne Image Grading with Label Distribution Smoothing
Kirill Prokhorov Alexandr A. Kalinin
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.