# A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification

arXiv: https://arxiv.org/abs/1703.01025 · HTML: https://mlsanity.com/paper/1703.01025/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2017.

## Built on by (most cited first, 13 of 13)

- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation](https://mlsanity.com/paper/2303.10049/) (2023)
- [Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification](https://mlsanity.com/paper/2308.05770/) (2023)
- [Data-Driven Deep Supervision for Skin Lesion Classification](https://mlsanity.com/paper/2209.01527/) (2022)
- [U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction](https://mlsanity.com/paper/2108.00860/) (2021)
- [Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis](https://mlsanity.com/paper/2505.06271/) (2025)
- [What Can We Learn from Inter-Annotator Variability in Skin Lesion Segmentation?](https://mlsanity.com/paper/2508.09381/) (2025)
- [RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation](https://mlsanity.com/paper/2604.27559/) (2026)
- [Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images](https://mlsanity.com/paper/2203.00641/) (2022)
- [GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis](https://mlsanity.com/paper/2301.04410/) (2023)
- [Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging](https://mlsanity.com/paper/2503.18170/) (2025)
- [Privileged Lesion-Context Relational Distillation for Mask-Free Skin Lesion Classification](https://mlsanity.com/paper/2607.18773/) (2026)
- [Less Is More: A Leakage-Controlled Study of Dermoscopic Preprocessing for Joint Skin Lesion Classification and Segmentation with YOLO26](https://mlsanity.com/paper/2610.08570/) (2026)

## Abstract (arXiv)

In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards Melanoma Detection, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.