# A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation

arXiv: https://arxiv.org/abs/1810.07075 · HTML: https://mlsanity.com/paper/1810.07075/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [A Comprehensive Review on Deep Supervision: Theories and Applications](https://mlsanity.com/paper/2207.02376/) (2022)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation](https://mlsanity.com/paper/2409.03062/) (2024)
- [Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoders](https://mlsanity.com/paper/2102.01824/) (2021)
- [One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data](https://mlsanity.com/paper/2307.15198/) (2023)
- [CSDN: Combing Shallow and Deep Networks for Accurate Real-time Segmentation of High-definition Intravascular Ultrasound Images](https://mlsanity.com/paper/2301.13648/) (2023)
- [LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion](https://mlsanity.com/paper/2305.00837/) (2023)
- [SLP-Net:An efficient lightweight network for segmentation of skin lesions](https://mlsanity.com/paper/2312.12789/) (2023)
- [Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks](https://mlsanity.com/paper/2305.09833/) (2023)
- [Channel Attention Separable Convolution Network for Skin Lesion Segmentation](https://mlsanity.com/paper/2309.01072/) (2023)

## Abstract (arXiv)

The computer-aided diagnosis (CAD) systems can highly improve the reliability and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion segmentation has the unsatisfactory accuracy in existing methods due to large variability in lesion appearance and artifacts. In this work, we propose a framework employing multi-stage UNets (MS-UNet) in the auto-context scheme to segment skin lesion accurately end-to-end. We apply two approaches to boost the performance of MS-UNet. First, UNet is coupled with a context information fusion structure (CIFS) to integrate the low-level and context information in the multi-scale feature space. Second, to alleviate the gradient vanishing problem, we use deep supervision mechanism through supervising MS-UNet by minimizing a weighted Jaccard distance loss function. Four out of five commonly used performance metrics, including Jaccard index and Dice coefficient, show that our approach outperforms the state-ofthe-art deep learning based methods on the ISBI 2016 Skin Lesion Challenge dataset.