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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2694 条记录，以下是261-270 订阅

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Histopathology image Classification Using Deep Manifold Contrastive Learning 26th

Histopathology Image Classification Using Deep Manifold Cont...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Tan, Jing Wei Jeong, Won-Ki Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9783031439865;9783031439872

Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly used similarity metric in contrastive learning, is not well suited to represent the distance between two data points, especially on a nonlinear feature manifold. Inspired by manifold learning, we propose a novel extension of contrastive learning that leverages geodesic distance between features as a similarity metric for histopathology whole slide image classification. To reduce the computational overhead in manifold learning, we propose geodesic-distance-based feature clustering for efficient contrastive loss evaluation using prototypes without time-consuming pairwise feature similarity comparison. the efficacy of the proposed method is evaluated on two real-world histopathology image datasets. Results demonstrate that our method outperforms state-of-the-art cosine-distance-based contrastive learning methods.

关键词： Contrastive learning Manifold learning Geodesic distance Histopathology image classification Multiple instance learning

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X-Ray to CT Rigid Registration Using Scene Coordinate Regression 26th

X-Ray to CT Rigid Registration Using Scene Coordinate Regres...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Shrestha, Pragyan Xie, Chun Shishido, Hidehiko Yoshii, Yuichi Kitahara, Itaru Univ Tsukuba Tsukuba Ibaraki Japan Tokyo Med Univ Ami Ibaraki Japan

ISBN: (纸本)9783031439988;9783031439995

Intraoperative fluoroscopy is a frequently used modality in minimally invasive orthopedic surgeries. Aligning the intraoperatively acquired X-ray image with the preoperatively acquired 3D model of a computed tomography (CT) scan reduces the mental burden on surgeons induced by the overlapping anatomical structures in the acquired images. this paper proposes a fully automatic registration method that is robust to extreme viewpoints and does not require manual annotation of landmark points during training. It is based on a fully convolutional neural network (CNN) that regresses the scene coordinates for a given X-ray image. the scene coordinates are defined as the intersection of the back-projected rays from a pixel toward the 3D model. Training data for a patient-specific model were generated through a realistic simulation of a C-arm device using preoperative CT scans. In contrast, intraoperative registration was achieved by solving the perspective-n-point (PnP) problem with a random sample and consensus (RANSAC) algorithm. Experiments were conducted using a pelvic CT dataset that included several real fluoroscopic (X-ray) images with ground truth annotations. the proposed method achieved an average mean target registration error (mTRE) of 3.79+/1.67mm in the 50th percentile of the simulated test dataset and projected mTRE of 9.65+/-4.07mm in the 50th percentile of real fluoroscopic images for pelvis registration. the code is available at https://***/Pragyanstha/SCR-Registration.

关键词： Registration X-Ray image Scene Coordinates

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MedIM: Boost medical image Representation via Radiology Report-Guided Masking 26th

MedIM: Boost Medical Image Representation via Radiology Repo...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Xie, Yutong Gu, Lin Harada, Tatsuya Zhang, Jianpeng Xia, Yong Wu, Qi Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia RIKEN AIP Tokyo Japan Univ Tokyo RCAST Tokyo Japan Northwestern Polytech Univ Sch Comp Sci & Engn Xian Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Masked image modelling (MIM)-based pre-training shows promise in improving image representations with limited annotated data by randomly masking image patches and reconstructing them. However, random masking may not be suitable for medical images due to their unique pathology characteristics. this paper proposes Masked medical image Modelling (MedIM), a novel approach, to our knowledge, the first research that masks and reconstructs discriminative areas guided by radiological reports, encouraging the network to explore the stronger semantic representations from medical images. We introduce two mutual comprehensive masking strategies, knowledge word-driven masking (KWM) and sentence-driven masking (SDM). KWM uses medical Subject Headings (MeSH) words unique to radiology reports to identify discriminative cues mapped to MeSH words and guide the mask generation. SDM considers that reports usually have multiple sentences, each of which describes different findings, and therefore integrates sentencelevel information to identify discriminative regions for mask generation. MedIM integrates both strategies by simultaneously restoring the images masked by KWM and SDM for a more robust and representative medical visual representation. Our extensive experiments on various downstream tasks covering multi-label/class image classification, medical image segmentation, and medical image-text analysis, demonstrate that MedIM with report-guided masking achieves competitive performance. Our method substantially outperforms imageNet pre-training, MIM-based pre-training, and medical image-report pre-training counterparts. Codes are available at https://***/YtongXie/MedIM.

关键词： Semantics

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Instructive Feature Enhancement for Dichotomous medical image Segmentation 26th

Instructive Feature Enhancement for Dichotomous Medical Imag...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Liu, Lian Zhou, Han Chen, Jiongquan Liu, Sijing Shi, Wenlong Ni, Dong Fan, Deng-Ping Yang, Xin Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China Swiss Fed Inst Technol Comp Vis Lab CVL Zurich Switzerland

ISBN: (纸本)9783031439001;9783031439018

Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance. However, existing networks tend to struggle with task-specific, heavy and complex designs to improve accuracy. they made little instructions to which feature channels would be more beneficial for segmentation, and that may be why the performance and universality of these segmentation models are hindered. In this study, we propose an instructive feature enhancement approach, namely IFE, to adaptively select feature channels with rich texture cues and strong discriminability to enhance raw features based on local curvature or global information entropy criteria. Being plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model to focus on texture-rich features which are especially important for the ambiguous and challenging boundary identification, simultaneously achieving simplicity, universality, and certain interpretability. To evaluate the proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k, which contains 55,023 images from 7 modalities and 26 anatomical structures. Extensive experiments show that IFE can improve the performance of classic segmentation networks across different anatomies and modalities with only slight modifications. Code is available at https://***/yezi-66/IFE.

关键词： Textures

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Robust Hough and Spatial-To-Angular Transform Based Rotation Estimation for Orthopedic X-Ray images 26th

Robust Hough and Spatial-To-Angular Transform Based Rotation...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Bachmaier, Magdalena Rohleder, Maximilian Swartman, Benedict Privalov, Maxim Maier, Andreas Kunze, Holger Friedrich Alexander Univ Erlangen Nurnberg Dept Comp Sci Pattern Recognit Lab Erlangen Germany Siemens Healthcare GmbH Forchheim Germany BG Trauma Ctr Ludwigshafen Dept Trauma & Orthopaed Surg Ludwigshafen Germany

ISBN: (纸本)9783031439896;9783031439902

Standardized image rotation is essential to improve reading performance in interventional X-ray imaging. To minimize user interaction and streamline the 2D imaging workflow, we present a new automated image rotation method. image rotation can follow two steps: First, an anatomy specific centerline image is predicted which depicts the desired anatomical axis to be aligned vertically after rotation. In a second step, the necessary rotation angle is calculated from the orientation of the predicted line image. We propose an end-to-end trainable model with the Hough transform (HT) and a differentiable spatial-to-angular transform (DSAT) embedded as known operators. this model allows to robustly regress a rotation angle while maintaining an explainable inner structure and allows to be trained with both a centerline segmentation and angle regression loss. the proposed method is compared to a Hu moments-based method on anterior-posterior X-ray images of spine, knee, and wrist. For the wrist images, the HT based method reduces the mean absolute angular error (MAE) from 9.28. using the Hu moments-based method to 3.54.. Similar results for the spinal and knee images can be reported. Furthermore, a large improvement of the 90th percentile of absolute angular error by a factor of 3 indicates a better robustness and reduction of outliers for the proposed method.

关键词： Machine Learning image Rotation Hough Transform

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Improving image-Based Precision Medicine with Uncertainty-Aware Causal Models 26th

Improving Image-Based Precision Medicine with Uncertainty-Aw...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Durso-Finley, Joshua Falet, Jean-Pierre Mehta, Raghav Arnold, Douglas L. Pawlowski, Nick Arbel, Tal McGill Univ Ctr Intelligent Machines Quebec City PQ Canada Microsoft Res Cambridge England McGill Univ Montreal Neurol Inst Montreal PQ Canada MILA Quebec AI Inst Quebec City PQ Canada

ISBN: (纸本)9783031439032;9783031439049

image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. this allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

关键词： Uncertainty analysis

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Morphology-Inspired Unsupervised Gland Segmentation via Selective Semantic Grouping 26th

Morphology-Inspired Unsupervised Gland Segmentation via Sele...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhang, Qixiang Li, Yi Xue, Cheng Li, Xiaomeng Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9783031439001;9783031439018

Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis. However, the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Existing unsupervised semantic segmentation methods encounter a huge challenge on gland images. they either over-segment a gland into many fractions or under-segment the gland regions by confusing many of them with the background. To overcome this challenge, our key insight is to introduce an empirical cue about gland morphology as extra knowledge to guide the segmentation process. To this end, we propose a novel Morphology-inspired method via Selective Semantic Grouping. We first leverage the empirical cue to selectively mine out proposals for gland sub-regions with variant appearances. then, a Morphology-aware Semantic Grouping module is employed to summarize the overall information about glands by explicitly grouping the semantics of their sub-region proposals. In this way, the final segmentation network could learn comprehensive knowledge about glands and produce well-delineated and complete predictions. We conduct experiments on the GlaS dataset and the CRAG dataset. Our method exceeds the second-best counterpart by over 10.56% at mIOU.

关键词： Whole Slide image Unsupervised Gland Segmentation Morphology-inspired Learning Semantic Grouping

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DiffMIC: Dual-Guidance Diffusion Network for medical image Classification 26th

DiffMIC: Dual-Guidance Diffusion Network for Medical Image C...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Yang, Yijun Fu, Huazhu Aviles-Rivero, Angelica I. Schonlieb, Carola-Bibiane Zhu, Lei Hong Kong Univ Sci & Technol Guangzhou Guangzhou Guangdong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore Univ Cambridge Cambridge England

ISBN: (纸本)9783031439865;9783031439872

Diffusion Probabilistic Models have recently shown remarkable performance in generative image modeling, attracting significant attention in the computer vision community. However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification. In this paper, we propose the first diffusion-based model (named DiffMIC) to address general medical image classification by eliminating unexpected noise and perturbations in medical images and robustly capturing semantic representation. To achieve this goal, we devise a dual conditional guidance strategy that conditions each diffusion step with multiple granularities to improve step-wise regional attention. Furthermore, we propose learning the mutual information in each granularity by enforcing Maximum-Mean Discrepancy regularization during the diffusion forward process. We evaluate the effectiveness of our DiffMIC on three medical classification tasks with different image modalities, including placental maturity grading on ultrasound images, skin lesion classification using dermatoscopic images, and diabetic retinopathy grading using fundus images. Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin, indicating the universality and effectiveness of the proposed model. Our code is publicly available at https://***/scott-yjyang/DiffMIC.

关键词： diffusion probabilistic model medical image classification placental maturity skin lesion diabetic retinopathy

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Gradient and Feature Conformity-Steered medical image Classification with Noisy Labels 26th

Gradient and Feature Conformity-Steered Medical Image Classi...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Xing, Xiaohan Chen, Zhen Gao, Zhifan Yuan, Yixuan Stanford Univ Dept Radiat Oncol Stanford CA USA Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot CAIR Hong Kong Peoples R China Sun Yat Sen Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Noisy annotations are inevitable in clinical practice due to the requirement of labeling efforts and expert domain knowledge. therefore, medical image classification with noisy labels is an important topic. A recently advanced paradigm in learning with noisy labels (LNL) first selects clean data with small-loss criterion, then formulates the LNL problem as semi-supervised learning (SSL) task and employs Mixup to augment the dataset. However, the small-loss criterion is vulnerable to noisy labels and the Mixup operation is prone to accumulate errors in pseudo labels. To tackle these issues, we present a two-stage framework with novel criteria for clean data selection and a more advanced Mixup method for SSL. In the clean data selection stage, based on the observation that gradient space reflects optimization dynamics and feature space is more robust to noisy labels, we propose two novel criteria, i.e., Gradient Conformity-based Selection (GCS) and Feature Conformity-based Selection (FCS), to select clean samples. Specifically, the GCS and FCS criteria identify clean data that better aligns with the class-wise optimization dynamics in the gradient space and principal eigenvector in the feature space. In the SSL stage, to effectively augment the dataset while mitigating disturbance of unreliable pseudo-labels, we propose a Sample Reliability-based Mixup (SRMix) method which selects mixup partners based on their spatial reliability, temporal stability, and prediction confidence. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods on two medical datasets with synthetic and real-world label noise. the code is available at https://***/hathawayxxh/FGCS-LNL.

关键词： Label noise Gradient conformity Feature eigenvector conformity Mixup

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Source-Free Domain Adaptation for medical image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning 26th

Source-Free Domain Adaptation for Medical Image Segmentation...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Yu, Qinji Xi, Nan Yuan, Junsong Zhou, Ziyu Dang, Kang Ding, Xiaowei Shanghai Jiao Tong Univ Shanghai Peoples R China SUNY Buffalo Buffalo NY USA VoxelCloud Inc Los Angeles CA 90024 USA

ISBN: (纸本)9783031439896;9783031439902

Unsupervised domain adaptation (UDA) has increasingly gained interests for its capacity to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. However, typical UDA methods require concurrent access to both the source and target domain data, which largely limits its application in medical scenarios where source data is often unavailable due to privacy concern. To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation. Specifically, in the prototype-anchored feature alignment stage, we first utilize the weights of the pre-trained pixel-wise classifier as source prototypes, which preserve the information of source features. then, we introduce the bi-directional transport to align the target features with class prototypes by minimizing its expected cost. On top of that, a contrastive learning stage is further devised to utilize those pixels with unreliable predictions for a more compact target feature distribution. Extensive experiments on a cross-modality medical segmentation task demonstrate the superiority of our method in large domain discrepancy settings compared with the state-of-the-art SFDA approaches and even some UDA methods. Code is available at: https://***/CSCYQJ/MICCAI23- ProtoContra-SFDA.

关键词： Domain adaptation Source-free Contrastive learning

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