Machine learning aims to solve a task with a certain algorithm or statistical model that is trained on data, with or without labels. As a subcategory of machine learning, deep learning achieves good performance with its flexibility on end-to-end representation learning and architecture design. Despite the successes of deep learning, the output of which can be sensitive to various factors. This work visits three sensitivity factors: distribution shifts, attacks, and human impact. One factor that can impair the performance of a deep net is a distribution shift between the training data and the test data. Depending on the availability of either data or label, some coping strategies for distribution shifts are domain adaptation, domain generalization, transfer learning and multi-domain learning. We first show how domain adaptation can help to mitigate the gap between historic and modern photos for visual place recognition. We show that this can be realized by focusing the network on the buildings rather than the background with an attention module. In addition, we introduce a domain adaptation loss to align the source domain and the target domain. We thenmove to domain generalization and show that learning domain invariant representations cannot lead to good performance for domain generalization. We suggest to relax the constraint of learning domain invariant representation by learning representations that guarantee a domain invariant posterior, but the resulting representations are not necessarily domain invariant. We coin this type of representation as hypothesis invariant representation. Finally, we study multi-domain learning and transfer learning with the application of deep learning to classify Parkinson’s disease. We show that a temporal attention mechanism is key for transferring useful information from large non-medical public video datasets to Parkinson videos. Weights are learned for various tasks involved in this Parkinson dataset to decide a final score for each single patient. A deep net is also sensitive to malicious attacks, e.g., adversarial classification attacks or explanation attacks. Adversarial classification attacks manipulate the classification result while explanation attacks change the explanation heatmap but do not alter the original classification results. We notice that the robustness to an adversarial classification attack is linked to the shape of the softmax function and can be improved by using a polynomial softRmax, which is based on a Cauchy class conditional distribution. This also shows that the performance of deep learning is sensitive to the choice of class conditional distribution. Regarding the explanation attacks, we design several ways to attack the GradCAM explanation heatmap to become a predetermined target explanation which does not explain the classification result. We further explore the influence of human trainers in hyperparameter tuning during the learning of deep nets. A user study is designed to explore the correlation between the performance of a network and the human trainer’s experience of deep learning. Experience of deep learning is found to be correlated with the performance of the deep net.@en

Objective: Myasthenia gravis (MG) is an autoimmune disease leading to fatigable muscle weakness. Extra-ocular and bulbar muscles are most commonly affected. We aimed to investigate whether facial weakness can be quantified automatically and used for diagnosis and disease monitoring. Methods: In this cross-sectional study, we analyzed video recordings of 70 MG patients and 69 healthy controls (HC) with two different methods. Facial weakness was first quantified with facial expression recognition software. Subsequently, a deep learning (DL) computer model was trained for the classification of diagnosis and disease severity using multiple cross-validations on videos of 50 patients and 50 controls. Results were validated using unseen videos of 20 MG patients and 19 HC. Results: Expression of anger (p = 0.026), fear (p = 0.003), and happiness (p < 0.001) was significantly decreased in MG compared to HC. Specific patterns of decreased facial movement were detectable in each emotion. Results of the DL model for diagnosis were as follows: area under the curve (AUC) of the receiver operator curve 0.75 (95% CI 0.65–0.85), sensitivity 0.76, specificity 0.76, and accuracy 76%. For disease severity: AUC 0.75 (95% CI 0.60–0.90), sensitivity 0.93, specificity 0.63, and accuracy 80%. Results of validation, diagnosis: AUC 0.82 (95% CI: 0.67–0.97), sensitivity 1.0, specificity 0.74, and accuracy 87%. For disease severity: AUC 0.88 (95% CI: 0.67–1.0), sensitivity 1.0, specificity 0.86, and accuracy 94%. Interpretation: Patterns of facial weakness can be detected with facial recognition software. Second, this study delivers a ‘proof of concept’ for a DL model that can distinguish MG from HC and classifies disease severity.

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Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

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We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how polynomiality can remedy the situation. This, among others, leads purposefully to random-level performance in the tails, away from the bulk of the training data. A directly related, simple, yet important technical novelty we subsequently present is softRmax: a reasoned alternative to the standard softmax function employed in contemporary (deep) neural networks. It is derived through linking the standard softmax to Gaussian class-conditional models, as employed in LDA, and replacing those by a polynomial alternative. We show that two aspects of softRmax, conservativeness and inherent gradient regularization, lead to robustness against adversarial attacks without gradient obfuscation.

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