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A Multitask Deep Learning Approach for Tumor Segmentation and Morphological Quantification in Acral Melanoma

Xiaodong An1, Liu Liu2, Mengmeng Liu3

  • 1School of Mechanical Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450046, Henan, China.

Journal of Imaging Informatics in Medicine
|March 31, 2026
PubMed
Summary

This study introduces an AI framework for analyzing acral melanoma from whole-slide images (WSIs). The system accurately classifies malignancy, segments tumors, and quantifies morphology, aiding in diagnosis.

Keywords:
Deep learningDiagnostic assistanceImage classificationImage segmentationMelanomaWSI

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Area of Science:

  • Digital pathology
  • Artificial intelligence in oncology
  • Computational imaging

Background:

  • Acral melanoma diagnosis relies on expert histopathological assessment of whole-slide images (WSIs).
  • Objective and reproducible quantification of melanoma features remains a challenge, impacting diagnostic consistency.

Purpose of the Study:

  • To develop and validate an integrated multitask deep learning framework for automated acral melanoma analysis from WSIs.
  • To sequentially perform classification, segmentation, and morphological quantification for enhanced diagnostic support.

Main Methods:

  • Construction of a multiscale WSI dataset with pixel-level annotations.
  • Implementation of an optimized ResNet50 classifier for malignancy screening.
  • Development of a novel D3C-RS-Unet incorporating dual dilated dynamic convolution and channel attention for tumor segmentation.
  • Automated extraction of key morphological indicators.

Main Results:

  • The classifier achieved high accuracy (0.8947 ± 0.0098) in malignancy screening.
  • The D3C-RS-Unet significantly outperformed existing models in segmentation (Dice: 0.8427 ± 0.0057; IoU: 0.8053 ± 0.0071).
  • Extracted morphological metrics demonstrated excellent agreement with manual annotations (ICCs > 0.93).

Conclusions:

  • The developed end-to-end framework provides objective and reproducible quantitative data for acral melanoma analysis.
  • This AI-driven approach shows significant potential to assist pathologists in diagnosis and standardize reporting.
  • Automated analysis of WSIs can improve the efficiency and consistency of acral melanoma assessment.