Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

By Yading Yuan, M. Chao, Yeh-Chi Lo. 2017

601 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Oncology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chunhua Shen
2. Yi Yang
3. P. Heng
4. Hao Chen
5. Lequan Yu
6. Zhedong Zheng
7. Yutong Xie
8. Jianpeng Zhang

In Epidemiology

1. Chunhua Shen
2. A. Ben Hamza
3. Hasib Zunair

In Oncology

1. P. Heng
2. Hao Chen
3. Lequan Yu
4. A. Ben Hamza
5. Hasib Zunair
6. Balázs Harangi

Citations in the corpus

• Image Segmentation Using Deep Learning: A Survey (2020, methodology): “FCNs have been applied to a variety of segmentation problems, such as brain tumor segmentation [31], instanceaware semantic segmentation [32], skin lesion segmentation [33], and iris segmentation [34].”
• A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification (2019, methodology): “Several DCNN-based skin lesion segmentation methods have been published [4], [6]–[10], [25].”
• Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation (2019, methodology): “[49] trained a 19-layer deep convolutional neural network in an end-to-end manner for skin lesion segmentation.”
• A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification (2018, methodology): “In [52], the deep FCN model was utilized to segment skin lesions from dermoscopy images [52].”
• Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (2018, unknown): “mentation approaches have gained great popularity [1]–[6].”
• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018, background): “The noticeable progress in deep learning has benefited many trials in medical image analysis, such as lesion segmentation or detection [1], [2], [3], [4], [5], diseases classification [6], [7], [8], [9], noise induction [10], image annotation [11], [12], registration [13], regression [14] and so on.”
• Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation (2021, result): “Deep neural networks, and in particular convolutional neural networks (CNNs), have been successfully applied to image segmentation, showing promising results in comparison with shallow methods [7,8].”