Deep Learning for Medical Anomaly Detection - a Survey Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes
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1 Deep Learning for Medical Anomaly Detection - A Survey Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes Abstract—Machine learning-based medical anomaly detection not follow the general pattern present in the dataset. This is is an important problem that has been extensively studied. a crucial task as anomalous observations correlate with types Numerous approaches have been proposed across various medical of problem or fault, such as structural defects, system or mal- application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe ware intrusions, production errors, financial frauds or health a lack of structured organisation of these diverse research problems. Despite the straightforward definition, identifying applications such that their advantages and limitations can anomalies is a challenging task in machine learning. One of be studied. The principal aim of this survey is to provide a the main challenges arises from the inconsistent behaviour thorough theoretical analysis of popular deep learning techniques of different anomalies, and the lack of constant definition of in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, what constitutes an anomaly [1], [2], [3]. For example, in a comparing and contrasting their architectural differences as well particular context a certain heart rate can be normal, while in as training algorithms. Furthermore, we provide a comprehensive a different context it could indicate a health concern. Further- overview of deep model interpretation strategies that can be used more, noisy data capture settings and/or dynamic changes in to interpret model decisions. In addition, we outline the key monitoring environments can lead normal examples to appear limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation. as out of distribution samples (i.e. abnormal), yielding higher false positive rates [4]. Hence, intelligent learning strategies Index Terms—Deep Learning, Anomaly detection, Machine with high modelling capacity are required to better segregate Learning, Temporal analysis the anomalous samples from normal data. I. INTRODUCTION Identifying data samples that do not fit the overall data B. Why are Medical Anomalies Different? distribution is the principle task in anomaly detection. Anoma- The diagram in Fig.1 illustrates the main stages with lies can arise due to various reasons such as noise in the respect to medical data processing with machine learning, data capture process, changes in underlying phenomenon, or and how each stage relates to anomaly detection. Collected due to new or previously unseen conditions in the captured physiological data is analysed and typically utilised for i) environment. Therefore, anomaly detection is a crucial task in prediction and/or ii) diagnosis. Prediction tasks include pre- medical signal analysis. dicting future states of physiological signals such as blood The dawn of deep learning has revolutionised the machine pressure, or other characteristics such as recovery rates. For learning field and it’s success has seeped into the domain of diagnosis tasks a portion of the data is analysed to recognise medical anomaly detection, which has resulted in a myriad of pathological signs of specific medical conditions. Anomaly research articles leveraging deep machine learning architec- detection relates to both prediction and diagnosis tasks, as it tures for medical anomaly detection. captures unique characteristics of the physiological data that The principal aim of this survey is to present a structured could offer information about the data or patient. and comprehensive review of this existing literature, systemat- arXiv:2012.02364v2 [cs.LG] 13 Apr 2021 ically comparing and contrasting methodologies. Furthermore, we provide an extensive investigation in to deep model inter- pretation strategies, which is critical when applying ‘black- Physiological Diagnose box’ deep models for medical diagnosis and to understand Data why a decision is reached. In addition, we summarise the Anomaly Detection challenges and limitations of existing research, and identify Prediction key future research directions, paving the way for the prevalent and effective application of deep learning in medical anomaly detection. Fig. 1: Illustration of the main stages in medical data process- ing and how anomaly detection relates to other stages. A. What are Anomalies? Anomaly detection is the task of identifying out of distribu- Similar to other application domains, medical anomaly tion examples. Simply put, it seeks to detect examples that do detection also inherits the challenges described in Sec. I-A. For instance, Fig.2 (a) illustrates two examples from the Kvasir T. Fernando, H. Gammulle, S. Denman, S. Sridharan and C. Fookes are with SAIVT, Queensland University of Technology, Australia (e-mail: endoscopy image dataset [5]. Despite the strong visual similar- [email protected].) ities, the left figure is an example from the normal-cecum class 2 while the right figure is an example from the ulcerative-colitis on specific medical application domains [12], [13], [14], [15], disease category. Another example is given in Fig.2 (b) which [16], there is no systematic review of deep learning based illustrates the diverse nature of the normal data in a typical medical anomaly detection techniques which would allow medical dataset. These examples are heart sound recordings readers to compare and contrast the strengths and weakness of from the PhysioNet Computing in Cardiology Challenge 2016 different deep learning techniques, and leverage those findings [6]. The top figure shows a clean normal heart sound record- for different medical application domains. Tab.I summarises ing. While the figure in the 2nd row represents a recording of these limitations. This paper directly addresses this need and the normal category that has been corrupted by noise during contributes a thorough theoretical analysis of popular deep data capture. Therefore, when modelling normal examples, the learning model architectures, including convolutional neural model should have the capacity to represent the diverse nature networks, recurrent neural networks, generative adversarial of the normal data distribution. Apart from these inherent chal- networks, auto encoders, and neural memory networks; and lenges, medical anomaly detection has additional hindrances their application to medial anomaly detection. Furthermore, we which are application specific. Firstly as the end application extensively analyse different model training strategies, includ- is primarily medical diagnosis, the test sensitivity (the ability ing unsupervised learning, supervised learning and multi-task to correctly identify the anomalous samples) is a decisive learning. and crucial factor, and the abnormality detection model is required to be highly accurate. Secondly, there are numerous TABLE I: Comparison of Our Survey to Other Related Studies Reference patient specific characteristics that contribute to dissimilarities [3] [2] [14] [13] [15] [12] [16] Proposed Unsupervised X X X X X X X Supervised among different data samples. For instance, in [7] the authors Algorithmic Approach X X X X X X X X Recurrent X X X X X X Multi-Task X have identified substantial differences among children from Auto-Encoders X X X Generative Adversarial Networks X X X X X different demographics with respect to their resting state in Network Architecture Neural Memory Networks X Long Short-Term Memory Networks X X X X X Gated Recurrent Units X X X X X EEG data. There are also substantial differences between MRI-based Anomaly Detection X X X Anomalies in Endoscopy Images Applications X X X different age groups, genders, etc. Therefore, when designing Heart Sound Anomalies X X Epileptic Seizures X X X Model Interpretation Methods X an accurate medical anomaly detection framework measures Medical Data Capturing Processes X should be taken to mitigate such hindrances. Considering these challenges, medical anomaly detection is often posed as a Moreover, this paper provides a comprehensive overview of supervised learning task [8], [1], where a supervision signal is deep model interpretation strategies that can be used to inter- presented for the model to learn to discriminate normal from pret model decisions. This analysis systematically illustrates abnormal examples. This is in contrast to other domains such how these methods generates model agnostic interpretations, as production defect detection or financial frauds detection, and the limitations of these methods when applied to medical where anomalies are detected in an unsupervised manner. data. Finally, this review details the limitations of existing deep C. Why use Deep Learning for Medical Anomaly Detection? medical anomaly detection approaches and lists key research directions, inspiring readers to direct their future investigations Deep learning is becoming increasingly popular among towards generalisable and interpretable deep medical anomaly researchers in biomedical engineering as it offers a way to detection frameworks, as well as probabilistic and causal address the above stated challenges. One prominent character- approaches which may reveal cause and effect relationships istics of deep learning is it’s ability to model non-linearity. In- within the data. creasing non-linearity