Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and are massively underdiagnosed. How do we reliably detect rare diseases with such low prevalence rates? How to further leverage patients with possibly uncertain diagnosis to improve detection? In this paper, we propose a Complementary pattern Augmentation (CONAN) framework for rare disease detection. CONAN combines ideas from both adversarial training and max-margin classification. It first learns self-attentive and hierarchical embedding for patient pattern characterization. Then, we develop a complementary generative adversarial networks (GAN) model to generate candidate positive and negative samples from the uncertain patients by encouraging a max-margin between classes. In addition, CONAN has a disease detector that serves as the discriminator during the adversarial training for identifying rare diseases. We evaluated CONAN on two disease detection tasks. For low prevalence inflammatory bowel disease (IBD) detection, CONAN achieved .96 precision recall area under the curve (PR-AUC) and 50.1% relative improvement over best baseline. For rare disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22 PR-AUC with 41.3% relative improvement over the best baseline.
Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are presented, but word transcripts are only available for the source domain speech. We present novel au…
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
XITASO GmbH（XITASO 有限公司）；Zuse School of Excellence in Reliable AI（Zuse 卓越可靠人工智能学院）；Technische Hochschule Ingolstadt（因戈尔施塔特应用技术大学）；Technische Hochschule Augsburg（奥格斯堡应用技术大学）
Courant Institute, New York University（纽约大学库朗研究所）；Imagen Technologies（伊马根科技公司）
Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases. In this work, we address the problem of classifying bone lesions from X-ray images by increasing the small number of positive samples in the training set. We propose a generative data augmentation approach based on a cycle-consistent generative adversarial network that synthesizes bone lesions on images without pathology. We pose the generative task as an image-patch translation problem that we optimize specifically for distinct bones…
University of Guelph（圭尔夫大学）；Vector Institute for Artificial Intelligence（人工智能向量研究所）；Element AI（元素人工智能公司）；University of Cambridge（剑桥大学）；Mila（米拉人工智能研究所）；Université de Montréal（蒙特利尔大学）；Concordia University（康考迪亚大学）
Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of vision and language. We consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. <cup, on, table>. However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. <cup, on, surfboard>. Despite each of the objec…
NVIDIA Corporation（英伟达公司）；MGH & BWH Center for Clinical Data Science（麻省总医院与布列根和妇女医院临床数据科学中心）；Mayo Clinic（妙佑医疗国际）
Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images…
Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen China（清华大学深圳国际研究生院，清华大学，深圳中国）；Department of Strategic and Advanced Interdisciplinary Research, Pengcheng Laboratory, Shenzhen China（战略与先进跨学科研究部，鹏城实验室，深圳中国）；Department of Astronomy, Tsinghua University, Beijing China（天文学系，清华大学，北京中国）；School of Mathematics and Physics, Qinghai University, Xining China（数学物理学院，青海大学，西宁中国）；Department of Astronomy, The Ohio State University, Columbus, OH 43210, USA（天文学系，俄亥俄州立大学，哥伦布，OH 43210，美国）；Center for Cosmology and AstroParticle Physics (CCAPP), The Ohio State University, Columbus, OH 43210, USA（宇宙学与天体粒子物理中心（CCAPP），俄亥俄州立大学，哥伦布，OH 43210，美国）
Wide-angle fisheye cameras are commonly used in automated driving for parking and low-speed navigation tasks. Four of such cameras form a surround-view system that provides a complete and detailed view of the vehicle. These cameras are directly exposed to harsh environmental settings and can get soiled very easily by mud, dust, water, frost. Soiling on the camera lens can severely degrade the visual perception algorithms, and a camera cleaning system triggered by a soiling detection algorithm is increasingly being deployed. While adverse weather conditions, such as rain, are getting attention …
Recently, social infrastructure is aging, and its predictive maintenance has become important issue. To monitor the state of infrastructures, bridge inspection is performed by human eye or bay drone. For diagnosis, primary damage region are recognized for repair targets. But, the degradation at worse level has rarely occurred, and the damage regions of interest are often narrow, so their ratio per image is extremely small pixel count, as experienced 0.6 to 1.5 percent. The both scarcity and imbalance property on the damage region of interest influences limited performance to detect damage. If …
Diffusion-Based Data Augmentation for Medical Image Segmentation
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Dept. of Engineering for Innovation Medicine, University of Verona（威尼斯大学创新医学工程系）
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
Universidad Industrial de Santander（桑坦德工业大学）；University of Toulouse（图卢兹大学）；Université Paul Sabatier Toulouse 3（图卢兹第三大学（保罗·萨巴蒂埃大学））；Bicêtre University Hospital（比塞特尔大学医院）；Assistance publique des hôpitaux de Paris（巴黎公共援助医院）；Université Claude Bernard Lyon 1（里昂第一大学（克洛德·贝尔纳里昂第一大学））
Detecting offensive language on social media is an important task. The ICWSM-2020 Data Challenge Task 2 is aimed at identifying offensive content using a crowd-sourced dataset containing 100k labelled tweets. The dataset, however, suffers from class imbalance, where certain labels are extremely rare compared with other classes (e.g, the hateful class is only 5% of the data). In this work, we present Dager (Data Augmenter), a generation-based data augmentation method, that improves the performance of classification on imbalanced and low-resource data such as the offensive language dataset. Dage…
Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break the alignment among views. This is problematic since multi-view data tend to be scarce and it is expensive to annotate. In this work we propose to solve this issue by introducing a new multi-view data augmentation pipeline that preserves alignment among views. Additionally to traditional augmentation of the input image we also propose a second …
Supervised training of neural networks requires large, diverse and well annotated data sets. In the medical field, this is often difficult to achieve due to constraints in time, expert knowledge and prevalence of an event. Artificial data augmentation can help to prevent overfitting and improve the detection of rare events as well as overall performance. However, most augmentation techniques use purely spatial transformations, which are not sufficient for video data with temporal correlations. In this paper, we present a novel methodology for workflow augmentation and demonstrate its benefit f…
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）
In several application areas, such as medical diagnosis, spam filtering, fraud detection, and seismic data analysis, it is very usual to find relevant classification tasks where some class occurrences are rare. This is the so called class imbalance problem, which is a challenge in machine learning. In this work, we propose the SeismoFlow a flow-based generative model to create synthetic samples, aiming to address the class imbalance. Inspired by the Glow model, it uses interpolation on the learned latent space to produce synthetic samples for one rare class. We apply our approach to the develo…
Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning
Computational Health Center & Helmholtz AI, Helmholtz Munich（计算健康中心与亥姆霍兹AI，亥姆霍兹慕尼黑）；Computer Aided Medical Procedures, Technical University of Munich（计算机辅助医疗程序，慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Medicine III, Ludwig Maximilian University Hospital（路德维希·马克西米利安大学医院第三内科）；Department of Physics, Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学物理系）；DKTK, German Cancer Consortium, partner site Munich（德国癌症联盟DKTK，慕尼黑合作站点）
ST Electronics - SUTD Cyber Security Laboratory（ST电子—新加坡科技设计大学网络安全实验室）；Singapore University of Technology and Design（新加坡科技设计大学）
Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. In particular, we propose to oversample infrequent normal samples - normal samples that occur with small probability, e.g., rare normal events. We show that th…
Princeton University（普林斯顿大学）；Booz Allen Hamilton（博思艾伦汉密尔顿）；Laboratory for Physical Sciences（物理科学实验室）
Data augmentation has been rare in the cyber security domain due to technical difficulties in altering data in a manner that is semantically consistent with the original data. This shortfall is particularly onerous given the unique difficulty of acquiring benign and malicious training data that runs into copyright restrictions, and that institutions like banks and governments receive targeted malware that will never exist in large quantities. We present MARVOLO, a binary mutator that programmatically grows malware (and benign) datasets in a manner that boosts the accuracy of ML-driven malware …
University of Electronic Science and Technology of China（电子科技大学）；University of Toronto（多伦多大学）；The Hospital for Sick Children (SickKids)（SickKids 儿童医院（病童医院））；Sinai Health System（西奈卫生系统）；University Health Network（大学健康网络）；Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detect…
Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection
Quantum Physics and Spintronics Team, LPMC, Faculty of Sciences Ben M'sick（量子物理与自旋电子团队，拉瓦尔学院，本·马西克科学学院）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（eBRAIN实验室，工程学院，纽约大学阿布扎比分校（NYUAD））；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（量子与拓扑系统中心（CQTS），NYUAD研究机构）
Spatiotemporal deep learning models for detection of rapid intensification in cyclones
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India（印度理工学院班加罗尔分校土木工程系）；Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics（过渡人工智能研究组，数学与统计学学院）；University of New South Wales, Sydney, Australia（新南威尔士大学悉尼分校）；Centre for Artificial Intelligence and Innovation, Pingla Institute（人工智能与创新中心，Pingla研究所）
