The remarkable performance of deep neural networks (DNNs) currently makes them the method of choice for solving linear inverse problems. They have been applied to super-resolve and restore images, as well as to reconstruct MR and CT images. In these applications, DNNs invert a forward operator by finding, via training data, a map between the measurements and the input images. It is then expected that the map is still valid for the test data. This framework, however, introduces measurement inconsistency during testing. We show that such inconsistency, which can be critical in domains like medical imaging or defense, is intimately related to the generalization error. We then propose a framework that post-processes the output of DNNs with an optimization algorithm that enforces measurement consistency. Experiments on MR images show that enforcing measurement consistency via our method can lead to large gains in reconstruction performance.
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University Hospital of Tübingen（蒂宾根大学医院）；Kantonsspital Winterthur（温特图尔州立医院）；GlaxoSmithKline（葛兰素史克）
Privacy concerns around sharing personally identifiable information are a major practical barrier to data sharing in medical research. However, in many cases, researchers have no interest in a particular individual's information but rather aim to derive insights at the level of cohorts. Here, we utilize Generative Adversarial Networks (GANs) to create derived medical imaging datasets consisting entirely of synthetic patient data. The synthetic images ideally have, in aggregate, similar statistical properties to those of a source dataset but do not contain sensitive personal information. We ass…
DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency
The Hong Kong Polytechnic University（香港理工大学）；Hong Kong Baptist University（香港浸会大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Decision support tools that rely on supervised learning require large amounts of expert annotations. Using past radiological reports obtained from hospital archiving systems has many advantages as training data above manual single-class labels: they are expert annotations available in large quantities, covering a population-representative variety of pathologies, and they provide additional context to pathology diagnoses, such as anatomical location and severity. Learning to auto-generate such reports from images present many challenges such as the difficulty in representing and generating long…
Automated data augmentation, which aims at engineering augmentation policy automatically, recently draw a growing research interest. Many previous auto-augmentation methods utilized a Density Matching strategy by evaluating policies in terms of the test-time augmentation performance. In this paper, we theoretically and empirically demonstrated the inconsistency between the train and validation set of small-scale medical image datasets, referred to as in-domain sampling bias. Next, we demonstrated that the in-domain sampling bias might cause the inefficiency of Density Matching. To address the …
Supervised learning has proved effective for medical image analysis. However, it can utilize only the small labeled portion of data; it fails to leverage the large amounts of unlabeled data that is often available in medical image datasets. Supervised models are further handicapped by domain shifts, when the labeled dataset, despite being large enough, fails to cover different protocols or ethnicities. In this paper, we introduce \emph{extreme consistency}, which overcomes the above limitations, by maximally leveraging unlabeled data from the same or a different domain in a teacher-student sem…
University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；RWTH Aachen University（亚琛工业大学）
The automated analysis of medical images is currently limited by technical and biological noise and bias. The same source tissue can be represented by vastly different images if the image acquisition or processing protocols vary. For an image analysis pipeline, it is crucial to compensate such biases to avoid misinterpretations. Here, we evaluate, compare, and improve existing generative model architectures to overcome domain shifts for immunofluorescence (IF) and Hematoxylin and Eosin (H&E) stained microscopy images. To determine the performance of the generative models, the original and tran…
Technical University of Munich（慕尼黑工业大学）；Siemens AG（西门子股份公司）
Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells. Hematologists usually label and count microscopy images of blood cells manually. In many cases, however, cells in different maturity states are difficult to distinguish, and in combination with image noise and subjectivity, humans are prone to make labeling mistakes. This results in labels that are often not reproducible, which can directly affect the diagnoses. We introduce TIMELY, a probabilistic model that combines pseudotime inference methods with inhomogeneous hidden Markov trees, which addresses this …
Airdoc（鹰瞳科技）；Harbin Engineering University（哈尔滨工程大学）；IIAI（国际人工智能研究院）；Monash University（莫纳什大学）；The University of Sydney（悉尼大学）
Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning from datasets with label noise is more challenging since medical imaging datasets tend to have asymmetric (class-dependent) noise and suffer from high observer variability. In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-…
Faculty of Science, Technology and Communication（科学、技术与传播学院）
Within this thesis we propose a platform for combining Augmented Reality (AR) hardware with machine learning in a user-oriented pipeline, offering to the medical staff an intuitive 3D visualization of volumetric Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) medical image segmentations inside the AR headset, that does not need human intervention for loading, processing and segmentation of medical images. The AR visualization, based on Microsoft HoloLens, employs a modular and thus scalable frontend-backend architecture for real-time visualizations on multiple AR headsets. As Con…
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；University of Montreal（蒙特利尔大学）；Polytechnique Montreal（蒙特利尔综合理工学院）；Mila, Quebec AI Institute（魁北克人工智能研究所Mila）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. The combination of a specified set of modalities, which is selected depending on the scenario and anatomical part being scanned, will provide medical practitioners with full information about the region of interest in the human body, hence the missing MRI sequences should be reimbursed. The compensation of the adverse impact of losing useful information owing to the lack of one or more modalities is a well-known challenge in the field of …
Centre for Intelligent Machines, McGill University（麦吉尔大学智能机器中心）
Although deep learning (DL) models have shown great success in many medical image analysis tasks, deployment of the resulting models into real clinical contexts requires: (1) that they exhibit robustness and fairness across different sub-populations, and (2) that the confidence in DL model predictions be accurately expressed in the form of uncertainties. Unfortunately, recent studies have indeed shown significant biases in DL models across demographic subgroups (e.g., race, sex, age) in the context of medical image analysis, indicating a lack of fairness in the models. Although several methods…
Unsupervised Patch-GAN with Targeted Patch Ranking for Fine-Grained Novelty Detection in Medical Imaging
the Southern University of Science and Technology（南方科技大学）；University of Oxford（牛津大学）；Tsinghua University（清华大学）；University of Warwick（华威大学）；Northwest University（西北大学）；Peng Cheng Lab（鹏城实验室）
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
Indian Institute of Technology Indore, India；R.M.D. Engineering College, Kavaraipettai, India；Jio Platforms Limited, India；Birla Institute of Technology \& Science Pilani, India
Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning
Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology（南方科技大学广东省类脑智能计算重点实验室）；Singapore Eye Research Institute（新加坡眼科研究所）
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Rice University（莱斯大学）；McGovern Medical School, UTHealth（德克萨斯大学健康科学中心麦戈文医学院）；The University of Maryland Medical Intelligent Imaging (UM2ii) Center（马里兰大学医学智能成像中心）
Budapest University of Technology and Economics（布达佩斯技术与经济大学）
A major challenge in medical image analysis is the automated detection of biomarkers from neuroimaging data. Traditional approaches, often based on image registration, are limited in capturing the high variability of cortical organisation across individuals. Deep learning methods have been shown to be successful in overcoming this difficulty, and some of them have even outperformed medical professionals on certain datasets. In this paper, we apply a deep neural network to analyse the cortical surface data of neonates, derived from the publicly available Developing Human Connectome Project (dHC…
Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation
Department of Computing, Abasyn University Islamabad Campus (AUIC)（阿巴斯扬大学伊斯兰堡校区计算机系）；School of Computer Science and Informatics, De Montfort University（德蒙福特大学计算机科学与信息学学院）；Naif Arab University for Security Sciences（纳夫阿拉伯安全科学大学）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Center of Excellence in Precision Medicine and Digital Health, Chulalongkorn University（朱拉隆功大学精准医学与数字健康卓越中心）；School of Biomedical Engineering, UNSW Sydney（新南威尔士大学悉尼分校生物医学工程学院）
Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation
Wuhan University of Technology（武汉理工大学）；Huazhong University of Science and Technology（华中科技大学）；Changzhou United lmaging Healthcare Surgical Technology Co. Ltd.（常州联影医疗手术技术有限公司）
提出Mask to Concept (M2C)框架，无需外部模块或重训练，仅用少量标注图像，通过可学习概念嵌入搜索和混合不确定性估计，实现SAM3在医学图像中的自动少样本标注，达到SOTA性能。
CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
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）；Medical Business Department, iFlytek Co.Ltd（讯飞股份有限公司医疗业务部）
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Science and Technology of China（中国科学技术大学）
Medical image super-resolution (SR) is an active research area that has many potential applications, including reducing scan time, bettering visual understanding, increasing robustness in downstream tasks, etc. However, applying deep-learning-based SR approaches for clinical applications often encounters issues of domain inconsistency, as the test data may be acquired by different machines or on different organs. In this work, we present a novel algorithm called domain adaptable volumetric super-resolution (DA-VSR) to better bridge the domain inconsistency gap. DA-VSR uses a unified feature ex…
Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annotations together with few strongly-annotated labels so as to achieve comparable segmentation performance in many unprofessional scenarios. However, these approaches only concentrate on the supervision inconsistency between strongly- and weakly-annotated instances but ignore the instance inconsistency inside the weakly-annotated instances, which inevitably leads to perfo…
Stanford University（斯坦福大学）；Wake Forest School of Medicine（维克森林医学院）
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field. Self-supervised learning (SSL) methods involving pretext tasks have shown promise in overcoming this requirement by first pretraining models using unlabeled data. In this work, we evaluate the efficacy of two SSL methods (inpainting-based pretext tasks of context prediction and context restoration) for CT and MRI image segmentation in label-limited scenarios, and investigate the effect of implementa…
Adaptive Stain Normalization for Cross-Domain Medical Histology
Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA（生物医学工程系，约翰霍普金斯大学，巴尔的摩，MD，USA）；Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Baltimore, MD, USA（约翰霍普金斯疟疾研究 institute，W. Harry Feinstone 分子微生物学与免疫学系，布隆伯格公共卫生学院，巴尔的摩，MD，USA）；Dept. of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA（医学系，约翰霍普金斯大学医学院，巴尔的摩，MD，USA）；Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA（电气与系统工程系，宾夕法尼亚大学，费城，PA，USA）
Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges
Université Polytechnique Hauts-de-France（上法兰西理工大学）；Université de Lille（里尔大学）；Junia（朱尼亚高等工程师学院）；University of the Basque Country UPV/EHU（巴斯克大学）；National engineering school of Sfax（斯法克斯国立工程学院）
In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality of image analysis. In general, the accuracy of this process may depend both on the experience of the microscopist and on the equipment sensitivity and specificity. A medical image could be corrupted by both intrinsic noise, due to the device limitations, and, by extrinsic signal perturbations during image acquisition. Nowadays, CAD deep learning applications pre-process images with image denoising models to reinforce learning and prediction. In this work, an innovative and lightweight deep multiscale convol…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；AI Lab, Lenovo Research（联想研究院人工智能实验室）；Lenovo Corporate Research & Development, Lenovo Ltd.（联想集团企业研发中心）
Though deep learning has achieved advanced performance recently, it remains a challenging task in the field of medical imaging, as obtaining reliable labeled training data is time-consuming and expensive. In this paper, we propose a double-uncertainty weighted method for semi-supervised segmentation based on the teacher-student model. The teacher model provides guidance for the student model by penalizing their inconsistent prediction on both labeled and unlabeled data. We train the teacher model using Bayesian deep learning to obtain double-uncertainty, i.e. segmentation uncertainty and featu…
Deep vessel segmentation with joint multi-prior encoding
LaTIM UMR 1101；Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；University Hospital of Brest（布雷斯特大学医院）；CONICET（阿根廷国家科学技术研究委员会）；ICube UMR 7357；CNRS（法国国家科学研究中心）；IMT Atlantique（大西洋电信学院）
Federated Foundation Model for GI Endoscopy Images
Lane Department of Computer Science and Electrical Engineering, West Virginia University（沃里奇大学计算机科学与电气工程系）；West Virginia University（沃里奇大学）；School of Medicine, West Virginia University（沃里奇大学医学院）；University of Memphis（密苏里州孟菲斯大学）；University of Aberdeen（阿伯丁大学）
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）
Breast cancer is a health problem that affects mainly the female population. An early detection increases the chances of effective treatment, improving the prognosis of the disease. In this regard, computational tools have been proposed to assist the specialist in interpreting the breast digital image exam, providing features for detecting and diagnosing tumors and cancerous cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing the false positives rate is still challenging. Texture descriptors have been quite popular in medical image analysis, particularly in histopath…
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Nanyang Technological University（南洋理工大学）
Retrieval-Guided Generation for Safer Histopathology Image Captioning
Kimia Lab, Department of Artificial Intelligence \& Informatics, Mayo Clinic, Rochester, MN, USA；Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA；Lane Department of Computer Science；Electrical Engineering, West Virginia University, Morgantown, WV, USA；Department of Computer Science；Engineering, Princeton University, Princeton, NJ, USA；Department of Computer Sciences, University of Wisconsin--Madison, Madison, WI, USA
Tencent Healthcare, Tencent（腾讯医疗健康，腾讯）；Beijing Tongren Hospital, Capital Medical University（首都医科大学附属北京同仁医院）
Medical images are generally labeled by multiple experts before the final ground-truth labels are determined. Consensus or disagreement among experts regarding individual images reflects the gradeability and difficulty levels of the image. However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded. In this paper, we aim to take advantage of the raw multi-rater gradings to improve the deep learning model performance for the glaucoma c…
DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion
Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院）；University of Science and Technology of China (USTC)（中国科学技术大学）；Beihang University（北京航空航天大学）
As interpretability has been pointed out as the obstacle to the adoption of Deep Neural Networks (DNNs), there is an increasing interest in solving a transparency issue to guarantee the impressive performance. In this paper, we demonstrate the efficiency of recent attribution techniques to explain the diagnostic decision by visualizing the significant factors in the input image. By utilizing the characteristics of objectness that DNNs have learned, fully decomposing the network prediction visualizes clear localization of target lesion. To verify our work, we conduct our experiments on Chest X-…
Cervical cancer threatens the health of women seriously. Radiotherapy is one of the main therapy methods but with high risk of acute hematologic toxicity. Delineating the bone marrow (BM) for sparing using computer tomography (CT) images to plan before radiotherapy can effectively avoid this risk. Comparing with magnetic resonance (MR) images, CT lacks the ability to express the activity of BM. Thus, in current clinical practice, medical practitioners manually delineate the BM on CT images by corresponding to MR images. However, the time?consuming delineating BM by hand cannot guarantee the ac…
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯劳厄尔瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院 Rigshospitalet）；University of Copenhagen（哥本哈根大学）
