https://www.ncbi.nlm.nih.gov/pubmed/29948436
Laterality Classification of Fundus Images Using Interpretable Deep Neural Network.
Author information
- 1
- Department of Statistics, University of Oxford, Oxford, UK.
- 2
- VUNO Inc., 6F, 507, Gangnam-daero, Seocho-gu, Seoul, Republic of Korea.
- 3
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea.
- 4
- VUNO Inc., 6F, 507, Gangnam-daero, Seocho-gu, Seoul, Republic of Korea. khwan.jung@vuno.co.
Abstract
In this paper, we aimed to understand and analyze the outputs of a convolutional neural network model that classifies the laterality of fundus images. Our model not only automatizes the classification process, which results in reducing the labors of clinicians, but also highlights the key regions in the image and evaluates the uncertainty for the decision with proper analytic tools. Our model was trained and tested with 25,911 fundus images (43.4% of macula-centered images and 28.3% each of superior and nasal retinal fundus images). Also, activation maps were generated to mark important regions in the image for the classification. Then, uncertainties were quantified to support explanations as to why certain images were incorrectly classified under the proposed model. Our model achieved a mean training accuracy of 99%, which is comparable to the performance of clinicians. Strong activations were detected at the location of optic disc and retinal blood vessels around the disc, which matches to the regions that clinicians attend when deciding the laterality. Uncertainty analysis discovered that misclassified images tend to accompany with high prediction uncertainties and are likely ungradable. We believe that visualization of informative regions and the estimation of uncertainty, along with presentation of the prediction result, would enhance the interpretability of neural network models in a way that clinicians can be benefitted from using the automatic classification system.
KEYWORDS:
Deep learning; Deep neural network; Fundus images; Interpretability; Laterality classification
- PMID:
- 29948436
- DOI:
- 10.1007/s10278-018-0099-2
영국, VUNO, 분당서울대 안과
2018년 1월
안저사진, 시신경위치와 주요 혈관을 파악하는 정도 (activation map...)
CNN (five blocks of convolutional layers of 3 × 3 kernels, 2 × 2 strides with a ReLU activation function, followed by two fully connected layers with a softmax activation function)
정확도 99%
...임상적 의미는 낮은 내용. non-medical approach.
https://link.springer.com/article/10.1007%2Fs10278-018-0099-2
