Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；National University of Singapore（新加坡国立大学）
Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner
备注：
To appear in the Proceedings of the 24 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego,California, USA. PMLR: Volume 130
Deep learning approaches often require huge datasets to achieve good generalization. This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations. For such sensitive tasks it is also important to provide the confidence in the predictions. Here, we propose a way to learn and use probabilistic labels to train accurate and calibrated deep networks from relatively small datasets. We observe gains of up to 22% in the accuracy of models trained with these labels, as compared with traditional approaches, in three classification tasks: diagnosis of hip dysplasia, fatty liver, and glaucoma. The outputs of models trained with probabilistic labels are calibrated, allowing the interpretation of its predictions as proper probabilities. We anticipate this approach will apply to other tasks where few training instances are available and expert knowledge can be encoded as probabilities.
Federal University of São Carlos（圣卡洛斯联邦大学）；University of Western São Paulo（西圣保罗大学）；São Paulo State University（圣保罗州立大学）
Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/or machines, for instance. Therefore, by means of probability estimates, one can take decisions to work better in a number of scenarios. In this paper, we propose a probabilistic-based Optimum Path Forest (OPF) classifier to handle with binary classification problems, and we show…
Automatic Construction of Pattern Classifiers Capable of Continuous Incremental Learning and Unlearning Tasks Based on Compact-Sized Probabilistic Neural Network
Department of Computer Engineering, College of Science & Technology（计算机工程系，科学与技术学院）
Toyota Motor Europe（丰田汽车欧洲公司）；Czech Technical University in Prague（捷克布拉格技术大学）
Cross entropy loss has served as the main objective function for classification-based tasks. Widely deployed for learning neural network classifiers, it shows both effectiveness and a probabilistic interpretation. Recently, after the success of self supervised contrastive representation learning methods, supervised contrastive methods have been proposed to learn representations and have shown superior and more robust performance, compared to solely training with cross entropy loss. However, cross entropy loss is still needed to train the final classification layer. In this work, we investigate…
In this paper, we propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier. Previous works tend to apply a threshold either on the classification scores or the loss function to reject the inputs that violate the assumption. However, these methods cannot achieve the low False Positive Ratio (FPR) required in safety applications. The proposed method is a rejection option based on hypothesis testing with probabilistic networks. With probabilistic networks, it is possible to estimate the distribution of outcomes instead of a single output. By…
Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output. Many existing attack algorithms cover various settings, from white-box to black-box classifiers, but typically assume that the answers are deterministic and often fail when they are not. We therefore propose a new adversarial decision-based attack specifically designed for classifiers with probabilistic outputs. It is based on the HopSkipJump attack by Chen et al. (2019, arXiv:1904.02144v5 ), a strong and query efficient decision-based attack originally designed for…
In this paper we propose a non-metric ranking-based representation of semantic similarity that allows natural aggregation of semantic information from multiple heterogeneous sources. We apply the ranking-based representation to zero-shot learning problems, and present deterministic and probabilistic zero-shot classifiers which can be built from pre-trained classifiers without retraining. We demonstrate their the advantages on two large real-world image datasets. In particular, we show that aggregating different sources of semantic information, including crowd-sourcing, leads to more accurate c…
NICTA；University of Queensland（昆士兰大学）；Queensland University of Technology（昆士兰科技大学）
Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analysed on an overlapping block-by-block basis. A low…
Current deep learning classifiers, carry out supervised learning and store class discriminatory information in a set of shared network weights. These weights cannot be easily altered to incrementally learn additional classes, since the classification weights all require retraining to prevent old class information from being lost and also require the previous training data to be present. We present a novel two stage architecture which couples visual feature learning with probabilistic models to represent each class in the form of a Gaussian Mixture Model. By using these independent class repres…
Monash University（莫纳什大学）；Defence Science and Technology Group, Department of Defence（国防部国防科学技术集团）
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Many adversarial attacks belong to the category of dense attacks, which generate adversarial examples by perturbing all the pixels of a natural image. To generate sparse perturbations, sparse attacks have been recently developed, which are usually independent attacks derived by modifying a dense attack's algorithm with sparsity regularisations, resulting in reduced attack efficiency. In this paper, we aim to tackle this task fr…
National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）
Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant…
Elsevier Science Publishers B.V.（爱思唯尔科学出版公司）；University of Winchester（温彻斯特大学）；Universidad de Granada（格拉纳达大学）；Universidad de Malaga（马拉加大学）；University of Leicester（莱斯特大学）；Hospital San Juan de Dios（圣胡安·德·迪奥斯医院）
The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), there have been more than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. It is extremely important the early detection of the disease, and the use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. However, this process requires clinicians to do it within a manual and time-consuming task, which is not ideal when trying to speed up the diagnosis. In this w…
CytoDiff: AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health（人工智能与健康研究所，慕尼黑德国环境健康研究中心）；Department of Medicine III, Ludwig-Maximilian-University Hospital（第三医学部，路德维希-马克西米利安大学医院）；DKTK, German Cancer Consortium（德国癌症联盟，DKTK）；Escola Superior de Comerç Internacional, Universitat Pompeu Fabra (ESCI-UPF)（国际商业学校，庞培法布拉大学（ESCI-UPF））
Imperial College London（帝国理工学院）；University of Twente（特文特大学）
We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts. Furthermore, in contrast to existing adaptation approaches, we also learn a target expert from available target data solely. Then, a single and confident classifier is obtained by combining the predictions fro…
Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study
German Cancer Research Center (DKFZ)（德国癌症研究中心）；University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg（海德堡鲁普雷希特-卡尔大学曼海姆医学中心）；DKFZ Hector Cancer Institute at the University Medical Center Mannheim（曼海姆医学中心DKFZ赫克托癌症研究所）；University Hospital Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院）；University Hospital (UKSH), Kiel（基尔大学医院（UKSH））
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Massachusetts Institute of Technology（麻省理工学院）
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-art deep learning classifiers trained to yield diagnostic labels from X-ray images are biased with respect to protected attributes. We train convolution neural networks to predict 14 diagnostic labels in 3 prominent public chest X-ray datasets: MIMIC-CXR, Chest-Xray8, CheXpert, as well as a multi-site aggregation of all those datasets. We evaluate the TPR disparity -- the differ…
Generative models have recently undergone significant advancement due to the diffusion models. The success of these models can be often attributed to their use of guidance techniques, such as classifier or classifier-free guidance, which provide effective mechanisms to trade-off between fidelity and diversity. However, these methods are not capable of guiding a generated image to be aware of its geometric configuration, e.g., depth, which hinders their application to areas that require a certain level of depth awareness. To address this limitation, we propose a novel guidance method for diffus…
h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective
Research Inst. of Trustworthy Autonomous Systems & Dept. of Computer Science and Engineering, SUSTech, China（可信自主系统研究所及计算机科学与工程系，深圳技术大学，中国）；Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Dept. of Computer Science and Engineering, SUSTech, and Peng Cheng Lab, China（脑启发智能计算广东省重点实验室、计算机科学与工程系，深圳技术大学，以及鹏城实验室，中国）；Faculty of Engineering and Information Technology, University of Technology Sydney, Australia（工程与信息技术学院，悉尼大学，澳大利亚）；Inst. of Biomedical Engineering, Dept. of Engineering Science, University of Oxford, UK（生物医学工程研究所、工程科学系，牛津大学，英国）
