The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons. This paper proposes a fully automatic framework to reconstruct retinal images of high spatial resolution and increased FOV from multiple low-resolution images captured with non-mydriatic, mobile and video-capable but low-cost cameras. Within the scope of one examination, we scan different regions on the retina by exploiting eye motion conducted by a patient guidance. Appropriate views for our mosaicing method are selected based on optic disk tracking to trace eye movements. For each view, one super-resolved image is reconstructed by fusion of multiple video frames. Finally, all super-resolved views are registered to a common reference using a novel polynomial registration scheme and combined by means of image mosaicing. We evaluated our framework for a mobile and low-cost video fundus camera. In our experiments, we reconstructed retinal images of up to 30° FOV from 10 complementary views of 15° FOV. An evaluation of the mosaics by human experts as well as a quantitative comparison to conventional color fundus images encourage the clinical usability of our framework.
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd（远见智能有限公司）；Tongren Hospital（同仁医院）
For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel semi-supervised manner. A small set of (nearly 100) images are incompletely labeled and used to supervise the network to detect keypoints on the vascular tree. To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch. By utilizing a keypoint-based improved triplet loss as its description loss, SuperRetina produces highly discriminativ…
Sri Venkateswara college of Engineering（斯里文卡特斯瓦拉工程学院）
Teleophthalmology holds a great potential to improve the quality, access, and affordability in health care. For patients, it can reduce the need for travel and provide the access to a superspecialist. Ophthalmology lends itself easily to telemedicine as it is a largely image based diagnosis. The main goal of the proposed system is to diagnose the type of disease in the retina and to automatically detect and segment retinal diseases without human supervision or interaction. The proposed system will diagnose the disease present in the retina using a neural network based classifier.The extent of …
Shanghai Jiao Tong University（上海交通大学）；EPFL（洛桑联邦理工学院）；Shanghai General Hospital（上海市第一人民医院）
Projection map (PM) from optical coherence tomography (OCT) B-scan is an important tool to diagnose retinal diseases, which typically requires retinal layer segmentation. In this study, we present a novel end-to-end framework to predict PMs from B-scans. Instead of segmenting retinal layers explicitly, we represent them implicitly as predicted coordinates. By pixel interpolation on uniformly sampled coordinates between retinal layers, the corresponding PMs could be easily obtained with pooling. Notably, all the operators are differentiable; therefore, this Differentiable Projection Module (DPM…
SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT
Christian Doppler Laboratory for Artificial Intelligence in Retina（基督教多普勒人工智能视网膜实验室）；Institute of Artificial Intelligence（人工智能研究所）；Center for Medical Data Science（医学数据科学中心）；Medical University of Vienna（维也纳医学大学）；Computational Imaging Research Lab（计算成像研究实验室）；Department of Biomedical Imaging and Image-Guided Therapy（生物医学成像与影像引导治疗部门）；Department of Ophthalmology and Optometry（眼科与视光学部门）
IBM Research - Australia（IBM澳大利亚研究院）；NYU Langone Eye Center（纽约大学兰贡眼科中心）；NYU School of Medicine（纽约大学医学院）
Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the lack of labeled data which is time-consuming and costly to obtain. In this paper, we propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network using limited labeled samples and a large number of unlabeled images. First, a teacher segmentation model is trained from the labeled samples using Bayesian deep learning. The trained model is used to g…
LUMOS: Universal Semi-Supervised OCT Retinal Layer Segmentation with Hierarchical Reliable Mutual Learning
Southern University of Science and Technology（南方科技大学）；Shenzhen Loop Area Institute（深圳河套学院）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Institute for Infocomm Research, A*STAR, Singapore（信息通信研究所（A*STAR，新加坡））
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require resource-intensive expert annotation. Semi-supervised generative adversarial network (GAN) approaches offer a means to learn from limited labeled data alongside larger unlabeled datasets, but have not been applied to discern fine-scale, sparse or localized features that define medical abnormalities. To overcome these limitations, we propose a patch-based semi-supervised learning approach and evaluate performance on c…
Beijing Institute of Technology（北京理工大学）；Beijing Institute of Ophthalmology（北京眼科研究所）；Beijing Tongren Hospital（北京同仁医院）
Cataracts, which are lenticular opacities that may occur at different lens locations, are the leading cause of visual impairment worldwide. Accurate and timely diagnosis can improve the quality of life of cataract patients. In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed. To obtain more appropriate features for the automatic grading, the Haar wavelet is improved according to the characteristics of retinal images. Retinal images of non-cataract, as well as mild, moderate, and severe cataracts, are automatically recognized using the…
Robert Bosch GmbH（罗伯特·博世有限公司）；TU Dortmund University（多特蒙德工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly sho…
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segmentation on OCT is important for the detection and monitoring of different retinal diseases, like Age-related Macular Disease (AMD) or Diabetic Retinopathy. However, the majority of state-of-the-art layer segmentation methods are based on purely supervised deep-learning, requiring a large amount of pixel-level annotated data that is expensive and hard to obtain. With this in mind, we introduce a semi-supervised paradi…
We explore the value of weak labels in learning transferable representations for medical images. Compared to hand-labeled datasets, weak or inexact labels can be acquired in large quantities at significantly lower cost and can provide useful training signals for data-hungry models such as deep neural networks. We consider weak labels in the form of pseudo-labels and propose a semi-weakly supervised contrastive learning (SWCL) framework for representation learning using semi-weakly annotated images. Specifically, we train a semi-supervised model to propagate labels from a small dataset consisti…
Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-free reference image for supervision. In this study, we present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. A diffusion process is defined by adding a sequence of Gaussian noise to self-…
According to the World Health Organization, 285 million people worldwide live with visual impairment. The most commonly used imaging technique for diagnosis in ophthalmology is optical coherence tomography (OCT). However, analysis of retinal OCT requires trained ophthalmologists and time, making a comprehensive early diagnosis unlikely. A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images. The performance of the tool in classifying retinal conditions was on par to that of trained medical experts. …
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for th…
Casey Eye Institute, Oregon Health & Science University（俄勒冈健康与科学大学凯西眼科研究所）；School of Physics and Optoelectronic Engineering, Foshan University（佛山大学物理与光电工程学院）；Department of Biomedical Engineering, Oregon Health & Science University（俄勒冈健康与科学大学生物医学工程系）
Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc.) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and 6 healthy controls). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training an…
Medical University of Vienna（维也纳医科大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supervised learning allows to learn new representation from available large unlabelled data without any expert knowledge. We propose a deep learning self-supervised approach to model disease progression from longitudinal retinal optical coherence tomography (OCT). Our self-supervised model takes benefit from a generic time-related task, by learning to estimate the time interval between pairs of scans acquired from the sa…
A BERT-Style Self-Supervised Learning CNN for Disease Identification from Retinal Images
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；McKeley School of Engineering, Washington University in St. Louis（工程学院，华盛顿大学圣路易斯分校）；Department of Neurology, Mayo Clinic（神经病学部，梅奥诊所）
UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（A*STAR））；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼科研究所，新加坡国家眼科中心）
University of Oxford（牛津大学）；Big Data Institute, University of Oxford（牛津大学大数据研究所）；School of Engineering, Newcastle University（纽卡斯尔大学工程学院）；Neura Health Group；Newcastle Eye Centre, Royal Victoria Infirmary（纽卡斯尔皇家维多利亚医院纽卡斯尔眼科中心）；Newcastle University（纽卡斯尔大学）；Newcastle Upon Tyne, UK（泰恩河畔纽卡斯尔（英国））
Optical coherence tomography (OCT) is a commonly-used method of extracting high resolution retinal information. Moreover there is an increasing demand for the automated retinal layer segmentation which facilitates the retinal disease diagnosis. In this paper, we propose a novel multiprediction guided attention network (MPG-Net) for automated retinal layer segmentation in OCT images. The proposed method consists of two major steps to strengthen the discriminative power of a U-shape Fully convolutional network (FCN) for reliable automated segmentation. Firstly, the feature refinement module whic…
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Stanford University School of Medicine（斯坦福大学医学院）
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases（眼科学国家重点实验室、中山眼科中心、中山大学、广东省眼科学重点实验室和视觉科学、广东省眼科临床研究中心）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Hainan International College, Minzu University of China（海南国际学院，中国民族大学）；School of Information Technology, Faculty of Business and Hospitality, Torrens University Australia（澳大利亚托伦斯大学信息科技学院，商业与酒店管理学院）；AIM for Health Lab, Faculty of IT, Monash University, Australia（健康AIM实验室，墨尔本大学IT学院，澳大利亚）；Department of Ophthalmology, Guangzhou First People’s Hospital, the Second Affiliated Hospital of South China University of Technology（广州第一人民医院，华南理工大学第二附属医院）；The Third Xiangya Hospital of Central South University（中南大学湘雅医学院第三医院）
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Xiamen University（厦门大学）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康医疗大数据国家研究院）；Tencent YouTu Lab（腾讯优图实验室）；Southern University of Science and Technology（南方科技大学）
Representation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging
Institute of Ophthalmology, University College London（伦敦大学学院眼科研究所）；Wake Forest University School of Medicine（维克森林大学医学院）；Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences（弗吉尼亚理工大学-维克森林大学生物医学工程与科学学院）；University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算机学院）；NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust（NIHR生物医学研究中心，摩尔菲尔兹眼科医院NHS基金会信托）
Simon Fraser University（西蒙弗雷泽大学）；Tencent YouTu Lab（腾讯优图实验室）
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye. It is widely used for the diagnosis of ophthalmic diseases with retinal alteration, such as layer deformation and fluid accumulation. In this paper, a novel framework was proposed to segment retinal layers with fluid presence. The main contribution of this study is two folds: 1) we developed a cascaded network framework to incorporate the prior structural knowledge; 2) we proposed a novel deep neural network based on U-Net an…
University of Nevada, Reno（内华达大学雷诺分校）；Houston Eye Associates（休斯顿眼科协会）
Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases. In addition, the lack of knowledge transferability and the need for unreasonably large datasets limit clinical application of current machine learning systems. To increase robustness, a better understanding of how the retinal subspace deformations lead to various levels of disease severity needs to be utilized for prioritizing disease-specific model details. In this paper we propose the use of diseas…
University of Bern（伯尔尼大学）；University Hospital of Bern（伯尔尼大学医院）
Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In practice, clinical diagnosis and treatment strategies are closely linked to biomarkers visible in OCT volumes and the ability to identify these plays an important role in the development of ophthalmic pharmaceutical products. In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume. We do so by adding a bidir…
When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images
Technical University of Munich（慕尼黑工业大学）；TUM University Hospital（慕尼黑工业大学医院）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Sapienza University of Rome（罗马第一大学）；Faculty of Medicine, University of Southampton（南安普顿大学医学院）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS信托基金）；Imperial College London（伦敦帝国学院）
University of Isfahan（伊斯法罕大学）；Duke University（杜克大学）；Duke University Medical Center（杜克大学医学中心）
Optical coherence tomography (OCT) helps ophthalmologists assess macular edema, accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is necessary for OCT-guided treatment management, which relies on a precise image segmentation step. As manual analysis of retinal fluids is a time-consuming, subjective, and error-prone task, there is increasing demand for fast and robust automatic solutions. In this study, a new convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. The model benefits from hierarchi…
University of California, Berkeley（加利福尼亚大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）；University of California, Davis（加利福尼亚大学戴维斯分校）
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-intensive annotations and are restricted to the specific trained task. Here, we employed an unsupervised network with Non-Parametric Instance Discrimination (NPID) to grade age-related macular degeneration (AMD) severity using fundus photographs from the Age-Related Eye Disease Study (AREDS). Our unsupervised algorithm demonstrated versatility across different AMD classification schemes without retraining, and achiev…
Existing supervised approaches didn't make use of the low-level features which are actually effective to this task. And another deficiency is that they didn't consider the relation between pixels, which means effective features are not extracted. In this paper, we proposed a novel convolutional neural network which make sufficient use of low-level features together with high-level features and involves atrous convolution to get multi-scale features which should be considered as effective features. Our model is tested on three standard benchmarks - DRIVE, STARE, and CHASE databases. The results…
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting
Stadtspital Zürich（苏黎世市立医院）；Spross Research Institute（斯普罗斯研究所）；Gutblick Research（古特布里克研究公司）；Balgrist University Hospital（巴尔格里斯特大学医院）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔德眼科医院国民保健服务基金会信托）；Institute of Ophthalmology, University College London（伦敦大学学院眼科研究所）；University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡国家眼科中心新加坡眼科研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；iScreen 2 Prevent LLC（iScreen 2 Prevent有限责任公司）；Bascom Palmer Eye Institute, University of Miami（迈阿密大学巴斯康帕默眼科研究所）；University of Heidelberg（海德堡大学）；Semmelweis University（塞梅尔魏斯大学）
Purpose: Convolutional neural networks can be trained to detect various conditions or patient traits based on retinal fundus photographs, some of which, such as the patient sex, are invisible to the expert human eye. Here we propose a methodology for explainable classification of fundus images to uncover the mechanism(s) by which CNNs successfully predict the labels. We used patient sex as a case study to validate our proposed methodology. Approach: First, we used a set of 4746 fundus images, including training, validation and test partitions, to fine-tune a pre-trained CNN on the sex classifi…
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities. In this proof-of-concept study, we propose unsupervised identification of anomalies as candidates for markers in retinal Optical Coherence Tomography (OCT) imaging…
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis
Hong Kong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data
Division of Engineering, New York University Abu Dhabi（纽约大学阿布扎赫尔分校工程系）；Institute for Healthier Living Abu Dhabi（阿布扎赫尔健康生活研究所）；Eye Institute at Cleveland Clinic Abu Dhabi（阿布扎赫尔克利夫兰医学中心眼科研究所）；Canberra Hospital（堪培拉医院）
Beihang University（北京航空航天大学）；National Institute of Informatics（国立情报学研究所）；Peng Cheng Laboratory（鹏城实验室）；Central South University（中南大学）
Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based meth…
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images
Dept. of Information Technology（信息技术系）；Dept. of Molecular, Cell and Developmental Biology（分子、细胞与发育生物学系）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective
Naif Arab University for Security Sciences（奈伊夫阿拉伯安全科学大学）；Charles Sturt University（查尔斯特大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Department of Medical Physics and Biomedical Engineering, UCL（伦敦大学学院医学物理与生物医学工程系）；NIHR Biomedical Research Centre at Moorfields Eye Hospital（莫菲尔德眼科医院NIHR生物医学研究中心）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/EPSRC介入与外科科学中心）；Research Center for Healthcare Data Science, Zhejiang Lab（之江实验室医疗健康数据科学研究中心）；UCL Respiratory, University College London（伦敦大学学院呼吸病学中心）；UCL Institute of Ophthalmology, University College London（伦敦大学学院眼科学研究所）
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-cla…
Quasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis
Tianjin University（天津大学）；Tianjin Key Laboratory of Ophthalmology and Visual Science（天津眼科学与视觉科学重点实验室）；Tianjin Eye Institute（天津眼科研究院）；Tianjin Eye Hospital（天津眼科医院）；Clinical College of Ophthalmology, Tianjin Medical University（天津医科大学临床医学院）；Department of Ophthalmology, The Second Affiliated Hospital of Nanchang University（南昌大学第二附属医院眼科部）；Nankai University Affiliated Eye Hospital（南开大学附属眼科医院）
University of Tehran（德黑兰大学）；Noor Ophthalmology Research Center, Noor Eye Hospital（努尔眼科研究中心，努尔眼科医院）；Henry Ford Health System（亨利·福特医疗系统）
Age-related macular degeneration (AMD) is the most common cause of blindness in developed countries, especially in people over 60 years of age. The workload of specialists and the healthcare system in this field has increased in recent years mainly due to the prevalence of population aging worldwide and the chronic nature of AMD. Recent developments in deep learning have provided a unique opportunity to develop fully automated diagnosis frameworks. Considering the presence of AMD-related retinal pathologies in varying sizes in OCT images, our objective was to propose a multi-scale convolutiona…
Medical University Vienna（维也纳医科大学）；University of Vienna（维也纳大学）；NVIDIA Corporation（英伟达公司）
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a-priori definitions of these regions, large-scale annotations, and a representative patient cohort in the training set. In contrast, anomaly detection is not limited to specific definitions of pathologies and allows for training on healthy samples without annotation. Anomalous regions can then serve as candidates for biomarker discovery. Knowledge about normal anatomical structure br…
