TY - JOUR A1 - Taleb, Aiham A1 - Rohrer, Csaba A1 - Bergner, Benjamin A1 - De Leon, Guilherme A1 - Rodrigues, Jonas Almeida A1 - Schwendicke, Falk A1 - Lippert, Christoph A1 - Krois, Joachim T1 - Self-supervised learning methods for label-efficient dental caries classification JF - Diagnostics : open access journal N2 - High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce >= 45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive. KW - unsupervised methods KW - self-supervised learning KW - representation learning KW - dental caries classification KW - data driven approaches KW - annotation KW - efficient deep learning Y1 - 2022 U6 - https://doi.org/10.3390/diagnostics12051237 SN - 2075-4418 VL - 12 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Rezaei, Mina A1 - Näppi, Janne J. A1 - Lippert, Christoph A1 - Meinel, Christoph A1 - Yoshida, Hiroyuki T1 - Generative multi-adversarial network for striking the right balance in abdominal image segmentation JF - International journal of computer assisted radiology and surgery N2 - Purpose: The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images. Method: The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017. Result: In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively. Conclusion: The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts. KW - imbalanced learning KW - generative multi-discriminative networks KW - semantic KW - segmentation KW - abdominal imaging Y1 - 2020 U6 - https://doi.org/10.1007/s11548-020-02254-4 SN - 1861-6410 SN - 1861-6429 VL - 15 IS - 11 SP - 1847 EP - 1858 PB - Springer CY - Berlin ER -