Explaining decisions of black-box classifiers is paramount in sensitive domains such as medical imaging since clinicians confidence is necessary for adoption. Various explanation approaches have been proposed, among which perturbation based approaches are very promising. Within this class of methods, we leverage a learning framework to produce our visual explanations method. From a given classifier, we train two generators to produce from an input image the so called similar and adversarial images. The similar image shall be classified as the input image whereas the adversarial shall not. Visual explanation is built as the difference between these two generated images. Using metrics from the literature, our method outperforms state-of-the-art approaches. The proposed approach is model-agnostic and has a low computation burden at prediction time. Thus, it is adapted for real-time systems. Finally, we show that random geometric augmentations applied to the original image play a regularization role that improves several previously proposed explanation methods. We validate our approach on a large chest X-ray database.
Simon Fraser University（西蒙弗雷泽大学）；The University of British Columbia（不列颠哥伦比亚大学）
Being able to explain the prediction to clinical end-users is a necessity to leverage the power of AI models for clinical decision support. For medical images, saliency maps are the most common form of explanation. The maps highlight important features for AI model's prediction. Although many saliency map methods have been proposed, it is unknown how well they perform on explaining decisions on multi-modal medical images, where each modality/channel carries distinct clinical meanings of the same underlying biomedical phenomenon. Understanding such modality-dependent features is essential for c…
Deep neural networks (DNN) have achieved unprecedented performance in computer-vision tasks almost ubiquitously in business, technology, and science. While substantial efforts are made to engineer highly accurate architectures and provide usable model explanations, most state-of-the-art approaches are first designed for natural vision and then translated to the medical domain. This dissertation seeks to address this gap by proposing novel architectures that integrate the domain-specific constraints of medical imaging into the DNN model and explanation design.
University of Porto（波尔图大学）；INESC TEC（葡萄牙系统与计算机工程研究所）
The use of Deep Learning in the medical field is hindered by the lack of interpretability. Case-based interpretability strategies can provide intuitive explanations for deep learning models' decisions, thus, enhancing trust. However, the resulting explanations threaten patient privacy, motivating the development of privacy-preserving methods compatible with the specifics of medical data. In this work, we analyze existing privacy-preserving methods and their respective capacity to anonymize medical data while preserving disease-related semantic features. We find that the PPRL-VGAN deep learning…
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space where there are severe potential repercussions, we need to develop methods to gain confidence in the models' decisions. To this end, we propose a novel medical imaging generative adversarial framework, medXGAN (medical eXplanation GAN), to visually explain what a medical classifier focuses on in its binary predictions. By encoding domain knowledge of medical images, we are able to disentangle anatomical structure an…
Eindhoven University of Technology（埃因霍温理工大学）；IT University of Copenhagen（哥本哈根信息技术大学）
Transfer learning is a commonly used strategy for medical image classification, especially via pretraining on source data and fine-tuning on target data. There is currently no consensus on how to choose appropriate source data, and in the literature we can find both evidence of favoring large natural image datasets such as ImageNet, and evidence of favoring more specialized medical datasets. In this paper we perform a systematic study with nine source datasets with natural or medical images, and three target medical datasets, all with 2D images. We find that ImageNet is the source leading to t…
Pre-trained Models Succeed in Medical Imaging with Representation Similarity Degradation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Chemical Sciences, University of Chinese Academy of Sciences（中国科学院大学化学科学学院）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
Eindhoven University of Technology（埃因霍温理工大学）；Erasmus Medical Center（伊拉斯谟医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）
Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the s…
University of Copenhagen（哥本哈根大学）；Eindhoven University of Technology（埃因霍温理工大学）；Hvidovre Hospital（维兹奥勒医院）；Bispebjerg Hospital（比斯佩比约医院）；Erasmus MC - University Medical Center Rotterdam（伊拉斯谟医学中心——鹿特丹大学医学中心）
Supervised feature learning using convolutional neural networks (CNNs) can provide concise and disease relevant representations of medical images. However, training CNNs requires annotated image data. Annotating medical images can be a time-consuming task and even expert annotations are subject to substantial inter- and intra-rater variability. Assessing visual similarity of images instead of indicating specific pathologies or estimating disease severity could allow non-experts to participate, help uncover new patterns, and possibly reduce rater variability. We consider the task of assessing e…
Aalto University（阿尔托大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
In the present paper we present the potential of Explainable Artificial Intelligence methods for decision-support in medical image analysis scenarios. With three types of explainable methods applied to the same medical image data set our aim was to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). The visual explanations were provided on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with the goal of increasing the health professionals' trust in the black box predictions. We implemented two post-hoc interpretable machine l…
Southeast University（东南大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Rennes 1（雷恩第一大学）；Western University（韦仕敦大学）；Case Western Reserve University（凯斯西储大学）
Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable measurement for the inter-image similarity, hindering the learning of consistent representation for same semantics. We investigate the challenging problem of this task, i.e., learning a consistent representation between images for a clustering effect of same semantic features. We propose a novel vis…
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右侧伊萨尔医院）；Institute for Artificial Intelligence and Computer Science in Medicine, Technical University of Munich（慕尼黑工业大学人工智能与医学计算机科学研究所）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；AI for Image-Guided Diagnosis and Therapy, Technical University of Munich（慕尼黑工业大学图像引导诊断与治疗人工智能研究所）
Towards Multi-dimensional Explanation Alignment for Medical Classification
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；HKUST(GZ)（香港科技大学（广州））；Tongji University（同济大学）；Ant Group（蚂蚁集团）；The University of Auckland（奥克兰大学）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心）
Simon Fraser University（西蒙弗雷泽大学）；The University of British Columbia（不列颠哥伦比亚大学）
Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed decision support from AI and comply with evidence-based medical practice. Applying XAI in clinical settings requires proper evaluation criteria to ensure the explanation technique is both technically sound and clinically useful, but specific support is lacking to achieve this goal. To bridge the research gap, we propose the Clinical XAI Guidelines that consist of five criteria a clinical XAI needs to be optimized for. The guidelines recommend choosing an explanation form based on Guideline 1 (G1)…
Georgia Institute of Technology（佐治亚理工学院）；University of Cambridge（剑桥大学）；Bern University Hospital（伯尔尼大学医院）；National Taiwan University（台湾大学）；University of California, Berkeley（加州大学伯克利分校）；Tri-Service General Hospital（三军总医院）；Beijing Friendship Hospital（北京友谊医院）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Amsterdam（阿姆斯特丹大学）
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologis…
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks
Hong Kong University of Science and Technology（香港科技大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Harvard University（哈佛大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis
Universidade da Beira Interior（贝拉内格罗大学）；NOVA LINCS（新里斯本大学计算机科学与信息学实验室）；INESC TEC（葡萄牙科技与工程计算系统研究所）；Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院）
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
University of Washington（华盛顿大学）；Paul G. Allen School of Computer Science, University of Washington（华盛顿大学保罗·G·艾伦计算机科学学院）；Department of Neurological Surgery, University of Washington（华盛顿大学神经外科（注：此处按要求提取到学院/大学级别，实际原文该机构属于华盛顿大学，故提取为华盛顿大学））
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医疗中心）；University of Cincinnati College of Medicine（辛辛那提大学医学院）；University of Cincinnati（辛辛那提大学）
Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervised registration categories, for example, fully supervised, dual supervised, and weakly supervised registration. Next, similarity-based as well as generative adversarial network (GAN)-based registration are presented as part of unsupervised registrat…
Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Southern University of Science and Technology（南方科技大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g., a pre-trained VGG, to obtain a latent representation. However, due to domain shift, pre-trained models from the natural image domain might not apply to other image domains, such as medical imaging. Notably, in medical imaging, evaluating the perceptual similarity is exclusively performed by specialists trained extensively in diverse medical fields. Thus, medical imaging remains devoid of task-specific, objective perc…
Recent research on spatial and spatio-temporal model checking provides novel image analysis methodologies, rooted in logical methods for topological spaces. Medical Imaging (MI) is a field where such methods show potential for ground-breaking innovation. Our starting point is SLCS, the Spatial Logic for Closure Spaces -- Closure Spaces being a generalisation of topological spaces, covering also discrete space structures -- and topochecker, a model-checker for SLCS (and extensions thereof). We introduce the logical language ImgQL ("Image Query Language"). ImgQL extends SLCS with logical operato…
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）深圳大数据研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significantly expensive cost for annotating data by experts. To fully exploit the easily available unlabeled data, we propose a novel Spatio-Temporal Structure Consistent (STSC) learning framework. Specifically, a gram matrix is derived to combine the spatial structure consistency and temporal structure consistency together. This gram matrix captures the structural similarity among the representations of different training sa…
Performance Benchmarking and Optimisation of Clustering Algorithms for Local and Non-Local Similarity Measure in Medical Image Analysis
Department of Mathematics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa（数学系，罗德斯大学）；Department of Statistics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa（统计系，罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch 7600, South Africa（理论与计算科学国家研究所）
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所IIS）；Nuremberg Institute of Technology Georg Simon Ohm（纽伦堡乔治·西蒙·欧姆技术学院）
Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However, without further enhancements many state-of-the-art machine learning methods are not suitable for medical application. The most important reasons are insufficient data set quality and the black-box behavior of machine learning algorithms such as Deep Learning models. Consequently, end-users cannot correct the model's decisions and the corresponding explanations. The latter is crucial for the trustworthiness of machi…
Princeton University（普林斯顿大学）；The Chinese University of Hong Kong（香港中文大学）；Microsoft（微软）；UCLA（加州大学洛杉矶分校）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Microsoft Research New England（微软新英格兰研究院）；VSCO
We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the BI-RADS lexicon. After expert annotation of the interpretable units, our proposed method is able to generate explanations for CNN mammogram classification that are consistent with ground truth radiology reports on the Di…
Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University Hospital Carl Gustav Carus（卡尔·古斯塔夫·卡鲁斯大学医院）
Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image. One of the pivotal clinical applications of medical image fusion is the merging of anatomical and functional modalities for fast diagnosis of malignant tissues. In this paper, we present a novel end-to-end unsupervised learning-based Convolutional Neural Network (CNN) for fusing the high and low frequency components of MRI-PET grayscale image pairs, publicly available at ADNI, by exploiting Structural Similarity Index (SSIM) as the …
Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detection
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Information Science and Technology, Eastern Institute of Technology (EIT)（东方理工大学信息科学与技术学院）；Stanford University（斯坦福大学）
Registration networks have shown great application potentials in medical image analysis. However, supervised training methods have a great demand for large and high-quality labeled datasets, which is time-consuming and sometimes impractical due to data sharing issues. Unsupervised image registration algorithms commonly employ intensity-based similarity measures as loss functions without any manual annotations. These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training. However, these metho…
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；Shengjing Hospital of China Medical University, China（中国医科大学附属盛京医院）
In image registration, many efforts have been devoted to the development of alternatives to the popular normalized mutual information criterion. Concurrently to these efforts, an increasing number of works have demonstrated that substantial gains in registration accuracy can also be achieved by aligning structural representations of images rather than images themselves. Following this research path, we propose a new method for mono- and multimodal image registration based on the alignment of regularized vector fields derived from structural information such as gradient vector flow fields, a te…
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI
College of Electronic Information and Optical Engineering, Nankai University（南开大学电子信息与光学工程学院）；Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology（天津市光电传感器与传感网络技术重点实验室）；Tianjin First Central Hospital, School of Medicine, Nankai University（南开大学医学院附属天津市第一中心医院）
Maastricht University（马斯特里赫特大学）；Maastricht University Medical Centre+（马斯特里赫特大学医学中心+）
Artificial Intelligence has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision making process. Therefore, the…
The George Washington University Medical Center（乔治华盛顿大学医学中心）
Recently, deep learning has become much more popular in computer vision area. The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especially, for medical images. In this regard, U-Net is the predominant approach to medical image segmentation task. The U-Net not only performs well in segmenting multimodal medical images generally, but also in some tough cases of them. However, we found that the classical U-Net architecture has limitation in several aspects. Therefore, we applied modifications: 1) designed efficient CNN architecture to replace encoder an…
