A Multitask Deep Learning Approach for Tumor Segmentation and Morphological Quantification in Acral Melanoma Whole-Slide Images.

Xiaodong An, Liu Liu, Mengmeng Liu, Jianwei Liu

Abstract read

In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiaodong AnSchool of Mechanical Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450046, Henan, China.

Liu LiuSchool of Mechanical Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450046, Henan, China. liuliu8514@163.com.

Mengmeng LiuDepartment of Pathology, The Third People's Hospital of Zhengzhou, Zhengzhou, 450044, China.

Jianwei LiuSchool of Mechanical Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450046, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents an integrated multitask deep learning framework for the automated analysis of acral melanoma from whole-slide images (WSIs). We constructed a multiscale WSI dataset with pixel-level annotations and developed a pipeline that sequentially performs classification, segmentation, and morphological quantification. An optimized ResNet50 classifier first screens for malignancy. For malignant cases, our novel D3C-RS-Unet, which integrates dual dilated dynamic convolution and channel attention mechanisms, performs precise tumor segmentation. Finally, key morphological indicators are automatically extracted. Using patient-level fivefold cross-validation, the classifier achieved an accuracy of 0.8947 ± 0.0098. The D3C-RS-Unet significantly outperformed baseline and state-of-the-art models (Dice: 0.8427 ± 0.0057; IoU: 0.8053 ± 0.0071; p

Indexed as

Deep learningDiagnostic assistanceImage classificationImage segmentationMelanomaWSI

Identifiers

PMID41917244

DOI10.1109/TMI.2016.2529665

What OpenQuestion holds

Textmetadata

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.