期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 24, 期 5, 页码 1405-1412出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2019.2949075
关键词
Feature extraction; Semantics; Biomedical optical imaging; Optical imaging; Lesions; Convolution; Computer architecture; Glaucoma diagnosis; clinical interpreta-tion; medical image processing
类别
资金
- National Natural Science Foundation of China [61702558, 61573380]
- Hunan Natural Science Foundation [2017JJ3411]
- Key R&D projects in Hunan [2017WK2074]
- National Key R&D Program of China [2017YFC0840104]
- Graduate Research and Innovation Project of Central South University [2019zzts587]
Despite the potential to revolutionise disease diagnosis by performing data-driven classification, clinical interpretability of ConvNet remains challenging. In this paper, a novel clinical interpretable ConvNet architecture is proposed not only for accurate glaucoma diagnosis but also for the more transparent interpretation by highlighting the distinct regions recognised by the network. To the best of our knowledge, this is the first work of providing the interpretable diagnosis of glaucoma with the popular deep learning model. We propose a novel scheme for aggregating features from different scales to promote the performance of glaucoma diagnosis, which we refer to as M-LAP. Moreover, by modelling the correspondence from binary diagnosis information to the spatial pixels, the proposed scheme generates glaucoma activations, which bridge the gap between global semantical diagnosis and precise location. In contrast to previous works, it can discover the distinguish local regions in fundus images as evidence for clinical interpretable glaucoma diagnosis. Experimental results, performed on the challenging ORIGA datasets, show that our method on glaucoma diagnosis outperforms state-of-the-art methods with the highest AUC (0.88). Remarkably, the extensive results, optic disc segmentation (dice of 0.9) and local disease focus localization based on the evidence map, demonstrate the effectiveness of our methods on clinical interpretability.
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