Medical image classification using synergic deep learning
By Jianpeng Zhang, Yutong Xie, Qi Wu, Yong Xia. Medical Image Anal., 2019
374 citations recorded by the source corpus.
&NA; The classification of medical images is an essential task in computer‐aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains challenging due to the significant intra‐class variation and inter‐class similarity caused by the diversity of imaging modalities and clinical pathologies. In this paper, we propose a synergic deep learning (SDL) model to address this issue by using multiple deep convolutional neural networks (DCNNs) simultaneously and enabling them to mutually learn from each other. Each pair of DCNNs has their learned image representation concatenated as the input of a synergic network, which has a fully connected structure that predicts whether the pair of input images belong to the same class. Thus, if one DCNN makes a correct classification, a mistake made by the other DCNN leads to a synergic error that serves as an extra force to update the model. This model can be trained end‐to‐end under the supervision of classification errors from DCNNs and synergic errors from each pair of DCNNs. Our experimental results on the ImageCLEF‐2015, ImageCLEF‐2016, ISIC‐2016, and ISIC‐2017 datasets indicate that the proposed SDL model achieves the state‐of‐the‐art performance in these medical image classification tasks.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/563964d518376309086a8998e31a31ee06676efe
How researchers see this paper
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In Artificial Intelligence
- Yutong Xie
- Jianpeng Zhang
- Yong Xia
- Chunhua Shen
- Anton van den Hengel
- Qi Wu
- Damien Teney
- Liang-Chieh Chen
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification (2019, methodology): “[18], [19] proposed the synergic deep learning (SDL) model that uses dual DCNNs to address the issues of intraclass variation and inter-class similarity.”