Active Learning methods create an optimized and labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Experiments on three medical image datasets show that our novel online active learning model requires significantly less labelings, is more accurate, and is more robust to class imbalances than existing methods. Our method is also more accurate and computationally efficient than the baseline model. Compared to random sampling and uncertainty sampling, the method uses 275 and 200 (out of 768) fewer labeled examples, respectively. For Diabetic Retinopathy detection, our method attains a 5.88% accuracy improvement over the baseline model when 80% of the dataset is labeled, and the model reaches baseline accuracy when only 40% is labeled. F I G U R E 1 Proposed Active Learning pipeline. To solve a supervised classification task, we will use a deep network (DN), an initial labeled dataset D t r ai n , an unlabeled dataset D or acl e , and an oracle who can label data. We desire to label as few examples as possible. Each active learning iteration, we use the DN to compute a feature embedding for all labeled examples in D t r ai n and the top M unlabeled examples from D or acl e with highest predictive entropy. We select and label oracle examples furthest in feature space from the centroid of all labeled examples. The oracle examples are selected one at a time, and the centroid updated after each labeling. We train the model on the expanded training set and repeat the process. In the online setting, the model weights are not reset between iterations and we use only the newly labeled examples and a subset of previously labeled examples.

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018). NIPS 2017 Competition on adversarial attacks and defenses.Co-organised by Alexey Kurakin. This competition pitted models against attacks but only indirectly: attacks were unable to query the models and hence had to device generic adversarial examples that would work against as many models as possible. Devising defenses against such unspecific transferbased attacks is much simpler than becoming robust against model-specific attacks.

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Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks.