# Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation

arXiv: https://arxiv.org/abs/2002.08694 · HTML: https://mlsanity.com/paper/2002.08694/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 15 of 15)

- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [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)
- [Towards Enhancing Fine-grained Details for Image Matting](https://mlsanity.com/paper/2101.09095/) (2021)
- [Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition](https://mlsanity.com/paper/2106.03455/) (2021)
- [Self-Regularized Prototypical Network for Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2210.16829/) (2022)
- [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://mlsanity.com/paper/2311.18373/) (2023)
- [TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation](https://mlsanity.com/paper/2408.09687/) (2024)
- [Spatial Feature Mapping for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2206.01831/) (2022)
- [SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation](https://mlsanity.com/paper/2409.08652/) (2024)
- [Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance](https://mlsanity.com/paper/2210.14645/) (2022)
- [Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations](https://mlsanity.com/paper/2410.16057/) (2024)
- [Contrastive and Selective Hidden Embeddings for Medical Image Segmentation](https://mlsanity.com/paper/2201.08779/) (2022)
- [Convolutional Neural Networks Towards Facial Skin Lesions Detection](https://mlsanity.com/paper/2402.08592/) (2024)
- [Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions](https://mlsanity.com/paper/2412.10816/) (2024)

## Abstract (arXiv)

Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.