عنوان انگلیسی مقاله:
Medical image classification using synergic deep learning
ترجمه فارسی عنوان مقاله:
طبقه بندی تصویر پزشکی با استفاده از یادگیری عمیق هم افزایی
Sciencedirect - Elsevier - Medical Image Analysis, 54 (2019) 10-19: doi:10:1016/j:media:2019:02:010
Jianpeng Zhang a , b , Yutong Xie a , b , Qi Wu b , Yong Xia a , c , ∗
The classification of medical images is an essential task in computer-aided diagnosis, medical image re- trieval 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 convo- lutional 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 net- work, 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 in- dicate that the proposed SDL model achieves the state-of-the-art performance in these medical image classification tasks.
Keywords: Medical image classification | Intra-class variation | Inter-class similarity | Synergic deep learning model