Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stable Diffusion have shown impressive generation capabilities, which aroused extensive discussion in the community. Many recent studies also found it is useful in many other vision tasks, like image deblurring, super-resolution and anomaly detection. Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff. In order to enhance the step-wise regional attention in DPM for the medical image segmentation, we propose dynamic conditional encoding, which establishes the state-adaptive conditions for each sampling step. We further propose Feature Frequency Parser (FF-Parser), to eliminate the negative effect of high-frequency noise component in this process. We verify MedSegDiff on three medical segmentation tasks with different image modalities, which are optic cup segmentation over fundus images, brain tumor segmentation over MRI images and thyroid nodule segmentation over ultrasound images. The experimental results show that MedSegDiff outperforms state-of-the-art (SOTA) methods with considerable performance gap, indicating the generalization and effectiveness of the proposed model. Our code is released at https://github.com/WuJunde/MedSegDiff.
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
National University of Singapore（新加坡国立大学）；University of Alberta（阿尔伯塔大学）；A*STAR（新加坡科技研究局）；Carnegie Mellon University（卡内基梅隆大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Oxford（牛津大学）；Kids with Tokens
the University of Hong Kong（香港大学）；China Agricultural University（中国农业大学）；Deepwise Healthcare（深睿医疗）
During clinical practice, radiologists often use attributes, e.g. morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning a…
Probabilistic smooth attention for deep multiple instance learning in medical imaging
Department of Computer Science and AI, University of Granada（计算机科学与人工智能系，格拉纳达大学）；Department of Statistics and Operations Research, University of Granada（统计与运筹学系，格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC), University of Granada（信息与通信技术研究中心（CITIC），格拉纳达大学）；Department of Electrical and Computer Engineering, Northwestern University（电气与计算机工程系，西北大学）；Center for Computational Imaging and Signal Analytics in Medicine, Northwestern University（医学计算成像与信号分析中心，西北大学）
We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frames, useful for temporal super-resolution. The model is based on a probabilistic latent space and a novel temporal dropout training scheme. This enables simulation and interpolation of realistic motion patterns given only one or any subset of frames of a sequence. The encoded motion also allows to be transported from one subject to another without the need of inter-subject registration. An unsupervised generative defor…
Encoding Structural Constraints into Segment Anything Models via Probabilistic Graphical Models
The George Washington University（乔治·华盛顿大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学苏瑞福公共卫生学院）；University of Rouen Normandy（鲁昂诺曼底大学）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
Peking University（北京大学）；Academy for Advanced Interdisciplinary Studies（前沿交叉学科研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM. Different from previous works, MC-DDPM is defined in measurement domain (e.g. k-space in MRI reconstruction) and conditioned on under-sampling mask. We apply this method to accelerate MRI reconstruction and the experimental results show excellent performance, outperforming full supervision baseline and the state-of-the-art score-based reconstruction method. Due to its generative nature, MC-DDPM can also quantify the unce…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Eindhoven University of Technology（埃因霍温理工大学）
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that u…
University Hospital Aachen（亚琛大学医院）；RWTH Aachen University（亚琛工业大学）；Ocumeda AG（奥库梅达股份公司）；University Hospital Heidelberg（海德堡大学医院）；University Hospital Würzburg（维尔茨堡大学医院）；University of Mainz（美因茨大学）；RWTH Aachen（亚琛工业大学）
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen and Stable Diffusion. However, their use in medicine, where image data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy preserving artificial intelligence and can also be used to augment small datasets. Here we show that diffusion probabilistic models can synthesize high quality medical imagi…
Baidu Research（百度研究院）；University of Technology Sydney（悉尼科技大学）
Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations…
DIKU University of Copenhagen（哥本哈根大学DIKU）；Biomediq A/S（Biomediq公司）；Cerebriu A/S（Cerebriu公司）；King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such transformations, we propose Probabilistic Augmentation of Data using Diffeomorphic Image Transformation (PADDIT) -- a systematic framework for generating realistic transformations that can be used to augment data for training CNNs. We show that CNNs trained with PADDIT outperforms CNNs trained without augmentation and with generic augmentation in segmen…
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
University of Calgary（卡尔加里大学）；McCaig Institute for Bone and Joint Health（麦凯格骨骼与关节健康研究所）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (AMII)（阿尔伯塔机器智能研究所）
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI
School of Biomedical Engineering（生物医学工程学院）；ShanghaiTech University（上海科技大学）；King’s College London（伦敦国王学院）；St. Thomas’ hospital（圣托马斯医院）；Institute of High Performance Computing（高性能计算研究所）
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；University of Cambridge（剑桥大学）
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
Faculty of Mathematics and Computer Science, Jagiellonian University（雅盖隆大学数学与计算机科学学院）；Faculty of Applied Computer Science, University of Information Technology and Management in Rzeszow（热舒夫信息技术与管理大学应用计算机科学学院）；Medical University of Gdansk（格但斯克医科大学）；UES Ltd.（UES有限公司）
Technical University of Munich（慕尼黑工业大学）；Massachusetts Eye and Ear Hospital（马萨诸塞眼耳医院）；Harvard Medical School（哈佛医学院）；LMU Munich（慕尼黑大学）
We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images. DVC simultaneously learns the multivariate Gaussian posterior through the probabilistic convolutional encoder and the likelihood distribution with the probabilistic convolutional decoder; and optimizes cluster labels assignment. Here, the learned multivariate Gaussian posterior captures the latent distribution of a large set of unlabeled images. Then, we perform unsupervised clustering on top of the variational latent space using a clustering loss. In …
Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Rutgers University（罗格斯大学）
Pancreas segmentation in medical imaging data is of great significance for clinical pancreas diagnostics and treatment. However, the large population variations in the pancreas shape and volume cause enormous segmentation difficulties, even for state-of-the-art algorithms utilizing fully-convolutional neural networks (FCNs). Specifically, pancreas segmentation suffers from the loss of spatial information in 2D methods, and the high computational cost of 3D methods. To alleviate these problems, we propose a probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture, which fu…
Wellcome Centre for Human Neuroimaging（惠康人类神经影像中心）；UCL（伦敦大学学院）；UCL Institute of Neurology（伦敦大学学院神经学研究所）
Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By allowing a common model for group-wise normalisation and segmentation of brain scans to handle missing data, the model allows for predicting entirely missing modalities from one, or a few, MR contrasts. Furthermore, the model can be trained on a fairly small number of subjects. The proposed model is validated on three clinically relevant scenarios. …
Scientific Computing and Imaging Institute（科学计算与成像研究所）；University of Utah（犹他大学）
Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks have increased the feasibility of adopting SSM in medical practice by reducing the expert-driven manual and computational overhead in traditional SSM workflows. However, translating such frameworks to clinical practice requires calibrated uncertainty measures as neural networks can produce over-confident predictions that cannot be trusted in sensitive clinical decision-making. Existin…
Siemens Healthineers（西门子医疗）；University of Michigan（密歇根大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. …
Federated Learning for Medical Image Classification: A Comprehensive Benchmark
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学吴贤铭智能工程学院）
Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects are challenging for existing computer vision algorithms for medical image analysis. Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees. The boundaries of such artifacts are ambiguous, leading to disagreement among human experts labeling the artifacts. We propose a weakly- and semi-supervised,…
Physics-Inspired Generative Models in Medical Imaging: A Review
KTH Royal Institute of Technology（瑞典皇家理工学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Microsoft Research（微软研究院）；TuSimple（图森未来）；Baidu Research（百度研究院）；Tencent Medical AI Labs（腾讯医疗AI实验室）；PingAn Tech（平安科技）
Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment recommendations require specially trained medical specialists. The current practice of reading medical images is labor-intensive, time-consuming, costly, and error-prone. It would be more desirable to have a computer-aided system that can automatically make diagnosis and treatment recommendations. Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images. In this t…
Centre for Intelligent Machines, McGill University（麦吉尔大学智能机器中心）；Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所）
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach--which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting--is non-parametric and thus offers a robust and consistent way to augment lesion segmentati…
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 …
ETH Zurich（苏黎世联邦理工学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Microsoft Mixed Reality and AI lab（微软混合现实与人工智能实验室）
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate well localised and high resolution models, evaluate prediction uncertainty, work with as little input data as possible. Current deep learning state of the art (SOTA) 3D reconstruction methods, however, often only produce shapes of limited variability positioned in a canonical position or lack uncertainty evaluation. In this paper, we present a novel probabilistic deep learning ap…
University of Burgundy（勃艮第大学）；Centre Georges François Leclerc (CGFL)（乔治·弗朗索瓦·勒克莱尔中心）；Institute of Curie（居里研究所）；University Hospital of Dijon（第戎大学医院）
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method for robust medical image segmentation by formulating the segmentation problem as a recurrent framework using two systems. The first one is a forward system of an encoder-decoder CNN that predicts the …
ProbMed: A Probabilistic Framework for Medical Multimodal Binding
Peter Munk Cardiac Centre（彼得·默克心脏中心）；Ted Rogers Centre for Heart Research（泰德·罗杰斯心脏病研究中心）；University Health Network（大学健康网络）；Joint Department of Medical Imaging（联合医学影像部门）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）
Recently, several Bayesian deep learning methods have been proposed for semi-supervised medical image segmentation. Although they have achieved promising results on medical benchmarks, some problems are still existing. Firstly, their overall architectures belong to the discriminative models, and hence, in the early stage of training, they only use labeled data for training, which might make them overfit to the labeled data. Secondly, in fact, they are only partially based on Bayesian deep learning, as their overall architectures are not designed under the Bayesian framework. However, unifying …
DeepMind（深度思维）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；German Cancer Research Center（德国癌症研究中心）
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation. This diversity and the variations of plausible interpretations are often specific to given image regions and may thus manifest on various scales, spanning all the way from the pixel to the image level. In order to learn a flexible distribution that can account for multiple scales of variations, we propose the Hierarchical Probabilistic U-Net, a segmentation network with a conditional vari…
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
West China Biomedical Big Data Center（西昌生物医学大数据中心）；West China Hospital（西昌医院）；Sichuan University（四川大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
University College London（伦敦大学学院）；Queen Mary University of London（伦敦玛丽女王大学）；University of Oxford（牛津大学）；University College London Hospitals NHS Trust（伦敦大学学院医院NHS信托基金会）；Imperial College Healthcare（帝国理工学院医疗保健）
