# Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks

arXiv: https://arxiv.org/abs/1709.09780 · HTML: https://mlsanity.com/paper/1709.09780/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 18 of 18)

- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition](https://mlsanity.com/paper/2106.03455/) (2021)
- [ConvFormer: Combining CNN and Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2211.08564/) (2022)
- [MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/2112.01767/) (2021)
- [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)
- [TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation](https://mlsanity.com/paper/2408.09687/) (2024)
- [Image Complexity Guided Network Compression for Biomedical Image Segmentation](https://mlsanity.com/paper/2107.02927/) (2021)
- [Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis](https://mlsanity.com/paper/2301.06226/) (2023)
- [Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods](https://mlsanity.com/paper/2408.03393/) (2024)
- [SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation](https://mlsanity.com/paper/2409.08652/) (2024)
- [HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation](https://mlsanity.com/paper/2109.05742/) (2021)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)
- [Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data](https://mlsanity.com/paper/2201.10650/) (2022)
- [GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis](https://mlsanity.com/paper/2301.04410/) (2023)
- [Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation](https://mlsanity.com/paper/2402.02649/) (2024)
- [GraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions](https://mlsanity.com/paper/2509.12277/) (2025)

## Abstract (arXiv)

Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index (JA) of 0.765 on the 600 challenge testing images, which ranked itself in the first place in the challenge