IEEE transactions on medical imaging最新文献_第8页

Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading 通过非对称双分类器差异最小化实现糖尿病视网膜病变分级的分级偏域自适应

IEEE transactions on medical imaging Pub Date : 2024-10-23 DOI: 10.1109/TMI.2024.3485064

Yuan Ma;Yang Gu;Shuai Guo;Xin Qin;Shijie Wen;Nianfeng Shi;Weiwei Dai;Yiqiang Chen

{"title":"Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading","authors":"Yuan Ma;Yang Gu;Shuai Guo;Xin Qin;Shijie Wen;Nianfeng Shi;Weiwei Dai;Yiqiang Chen","doi":"10.1109/TMI.2024.3485064","DOIUrl":"10.1109/TMI.2024.3485064","url":null,"abstract":"Diabetic retinopathy (DR) is a leading cause of preventable low vision worldwide. Deep learning has exhibited promising performance in the grading of DR. Certain deep learning strategies have facilitated convenient regular eye check-ups, which are crucial for managing DR and preventing severe visual impairment. However, the generalization performance on cross-center, cross-vendor, and cross-user test datasets is compromised due to domain shift. Furthermore, the presence of small lesions and the imbalanced grade distribution, resulting from the characteristics of DR grading (e.g., the progressive nature of DR disease and the design of grading standards), complicates image-level domain adaptation for DR grading. The general predictions of the models trained on grade-skewed source domains will be significantly biased toward the majority grades, which further increases the adaptation difficulty. We formulate this problem as a grade-skewed domain adaptation challenge. Under the grade-skewed domain adaptation problem, we propose a novel method for image-level supervised DR grading via Asymmetric Bi-Classifier Discrepancy Minimization (ABiD). First, we propose optimizing the feature extractor by minimizing the discrepancy between the predictions of the asymmetric bi-classifier based on two classification criteria to encourage the exploration of crucial features in adjacent grades and stretch the distribution of adjacent grades in the latent space. Moreover, the classifier difference is maximized by using the forward and inverse distribution compensation mechanism to locate easily confused instances, which avoids pseudo-label bias on the target domain. The experimental results on two public DR datasets and one private DR dataset demonstrate that our method outperforms state-of-the-art methods significantly.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1115-1126"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning 利用概率表征和原型学习实现有效的半监督医学图像分割

IEEE transactions on medical imaging Pub Date : 2024-10-21 DOI: 10.1109/TMI.2024.3484166

Yuchen Yuan;Xi Wang;Xikai Yang;Pheng-Ann Heng

{"title":"Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning","authors":"Yuchen Yuan;Xi Wang;Xikai Yang;Pheng-Ann Heng","doi":"10.1109/TMI.2024.3484166","DOIUrl":"10.1109/TMI.2024.3484166","url":null,"abstract":"Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous literature. To address this issue, we propose a probabilistic prototype-based classifier that introduces uncertainty estimation into the entire pixel classification process, including probabilistic representation formulation, probabilistic pixel-prototype proximity matching, and distribution prototype update, leveraging principles from probability theory. By explicitly modeling data uncertainty at the pixel level, model robustness of our proposed framework to tricky pixels, such as ambiguous boundaries and noises, is greatly enhanced when compared to its deterministic counterpart and other uncertainty-aware strategy. Empirical evaluations on three publicly available datasets that exhibit severe boundary ambiguity show the superiority of our method over several competitors. Moreover, our method also demonstrates a stronger model robustness to simulated noisy data. Code is available at <uri>https://github.com/IsYuchenYuan/PPC</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1181-1193"},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10723767","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT DPI-MoCo：用于 4D CBCT 的深度先验图像约束运动补偿重建技术

IEEE transactions on medical imaging Pub Date : 2024-10-18 DOI: 10.1109/TMI.2024.3483451

Dianlin Hu;ChenCheng Zhang;Xuanjia Fei;Yi Yao;Yan Xi;Jin Liu;Yikun Zhang;Gouenou Coatrieux;Jean Louis Coatrieux;Yang Chen

{"title":"DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT","authors":"Dianlin Hu;ChenCheng Zhang;Xuanjia Fei;Yi Yao;Yan Xi;Jin Liu;Yikun Zhang;Gouenou Coatrieux;Jean Louis Coatrieux;Yang Chen","doi":"10.1109/TMI.2024.3483451","DOIUrl":"10.1109/TMI.2024.3483451","url":null,"abstract":"4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods heavily rely on large labeled training datasets which are difficult to obtain in practical scenarios. Restricted by this dilemma, DL models often struggle with simultaneously retaining dynamic motions, removing streak degradations, and recovering fine details. To address the above challenging problem, we introduce a Deep Prior Image Constrained Motion Compensation framework (DPI-MoCo) that decouples the 4D CBCT reconstruction into two sub-tasks including coarse image restoration and structural detail fine-tuning. In the first stage, the proposed DPI-MoCo combines the prior image guidance, generative adversarial network, and contrastive learning to globally suppress the artifacts while maintaining the respiratory movements. After that, to further enhance the local anatomical structures, the motion estimation and compensation technique is adopted. Notably, our framework is performed without the need for paired datasets, ensuring practicality in clinical cases. In the Monte Carlo simulation dataset, the DPI-MoCo achieves competitive quantitative performance compared to the state-of-the-art (SOTA) methods. Furthermore, we test DPI-MoCo in clinical lung cancer datasets, and experiments validate that DPI-MoCo not only restores small anatomical structures and lesions but also preserves motion information.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1243-1256"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-Supervised Medical Image Segmentation FedLPPA：为联合弱监督医学图像分割学习个性化提示和聚合

IEEE transactions on medical imaging Pub Date : 2024-10-18 DOI: 10.1109/TMI.2024.3483221

Li Lin;Yixiang Liu;Jiewei Wu;Pujin Cheng;Zhiyuan Cai;Kenneth K. Y. Wong;Xiaoying Tang

{"title":"FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-Supervised Medical Image Segmentation","authors":"Li Lin;Yixiang Liu;Jiewei Wu;Pujin Cheng;Zhiyuan Cai;Kenneth K. Y. Wong;Xiaoying Tang","doi":"10.1109/TMI.2024.3483221","DOIUrl":"10.1109/TMI.2024.3483221","url":null,"abstract":"Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available at <uri>https://github.com/llmir/FedLPPA</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1127-1139"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STARTS: A Self-Adapted Spatio-Temporal Framework for Automatic E/MEG Source Imaging STARTS：用于自动 E/MEG 信号源成像的自适应时空框架

IEEE transactions on medical imaging Pub Date : 2024-10-18 DOI: 10.1109/TMI.2024.3483292

Zhao Feng;Cuntai Guan;Ruifeng Zheng;Yu Sun

{"title":"STARTS: A Self-Adapted Spatio-Temporal Framework for Automatic E/MEG Source Imaging","authors":"Zhao Feng;Cuntai Guan;Ruifeng Zheng;Yu Sun","doi":"10.1109/TMI.2024.3483292","DOIUrl":"10.1109/TMI.2024.3483292","url":null,"abstract":"To obtain accurate brain source activities, the highly ill-posed source imaging of electro- and magneto-encephalography (E/MEG) requires proficiency in incorporation of biophysiological constraints and signal-processing techniques. Here, we propose a spatio-temporal-constrainted E/MEG source imaging framework-STARTS that can reconstruct the source in a fully automatic way. Specifically, a block-diagonal covariance was adopted to reconstruct the source extents while maintain spatial homogeneity. Temporal basis functions (TBFs) of both sources and noise were estimated and updated in a data-driven fashion to alleviate the influence of noises and further improve source localization accuracy. The performance of the proposed STARTS was quantitatively assessed through a series of simulation experiments, wherein superior results were obtained in comparison with the benchmark ESI algorithms (including LORETA, EBI-Convex, BESTIES & SI-STBF). Additional validations on epileptic and resting-state EEG data further indicate that the STARTS can produce neurophysiologically plausible results. Moreover, a computationally efficient version of STARTS: smooth STARTS was also introduced with an elementary spatial constraint, which exhibited comparable performance and reduced execution cost. In sum, the proposed STARTS, with its advanced spatio-temporal constraints and self-adapted update operation, provides an effective and efficient approach for E/MEG source imaging.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1230-1242"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MRI Super-Resolution With Partial Diffusion Models 磁共振成像超分辨率与部分扩散模型

IEEE transactions on medical imaging Pub Date : 2024-10-17 DOI: 10.1109/TMI.2024.3483109

Kai Zhao;Kaifeng Pang;Alex Ling Yu Hung;Haoxin Zheng;Ran Yan;Kyunghyun Sung

{"title":"MRI Super-Resolution With Partial Diffusion Models","authors":"Kai Zhao;Kaifeng Pang;Alex Ling Yu Hung;Haoxin Zheng;Ran Yan;Kyunghyun Sung","doi":"10.1109/TMI.2024.3483109","DOIUrl":"10.1109/TMI.2024.3483109","url":null,"abstract":"Diffusion models have achieved impressive performance on various image generation tasks, including image super-resolution. Despite their impressive performance, diffusion models suffer from high computational costs due to the large number of denoising steps. In this paper, we proposed a novel accelerated diffusion model, termed Partial Diffusion Models (PDMs), for magnetic resonance imaging (MRI) super-resolution. We observed that the latents of diffusing a pair of low- and high-resolution images gradually converge and become indistinguishable after a certain noise level. This inspires us to use certain low-resolution latent to approximate corresponding high-resolution latent. With the approximation, we can skip part of the diffusion and denoising steps, reducing the computation in training and inference. To mitigate the approximation error, we further introduced ‘latent alignment’ that gradually interpolates and approaches the high-resolution latents from the low-resolution latents. Partial diffusion models, in conjunction with latent alignment, essentially establish a new trajectory where the latents, unlike those in original diffusion models, gradually transition from low-resolution to high-resolution images. Experiments on three MRI datasets demonstrate that partial diffusion models achieve competetive super-resolution quality with significantly fewer denoising steps than original diffusion models. In addition, they can be incorporated with recent accelerated diffusion models to further enhance the efficiency.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1194-1205"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CQformer: Learning Dynamics Across Slices in Medical Image Segmentation CQformer：医学图像分割中的跨切片动态学习

IEEE transactions on medical imaging Pub Date : 2024-10-10 DOI: 10.1109/TMI.2024.3477555

Shengjie Zhang;Xin Shen;Xiang Chen;Ziqi Yu;Bohan Ren;Haibo Yang;Xiao-Yong Zhang;Yuan Zhou

引用次数: 0

Non-Invasive Deep-Brain Imaging With 3D Integrated Photoacoustic Tomography and Ultrasound Localization Microscopy (3D-PAULM) 利用三维集成光声层析成像和超声定位显微镜（3D-PAULM）进行无创深部脑成像。

IEEE transactions on medical imaging Pub Date : 2024-10-09 DOI: 10.1109/TMI.2024.3477317

Yuqi Tang;Nanchao Wang;Zhijie Dong;Matthew Lowerison;Angela del Aguila;Natalie Johnston;Tri Vu;Chenshuo Ma;Yirui Xu;Wei Yang;Pengfei Song;Junjie Yao

{"title":"Non-Invasive Deep-Brain Imaging With 3D Integrated Photoacoustic Tomography and Ultrasound Localization Microscopy (3D-PAULM)","authors":"Yuqi Tang;Nanchao Wang;Zhijie Dong;Matthew Lowerison;Angela del Aguila;Natalie Johnston;Tri Vu;Chenshuo Ma;Yirui Xu;Wei Yang;Pengfei Song;Junjie Yao","doi":"10.1109/TMI.2024.3477317","DOIUrl":"10.1109/TMI.2024.3477317","url":null,"abstract":"Photoacoustic computed tomography (PACT) is a proven technology for imaging hemodynamics in deep brain of small animal models. PACT is inherently compatible with ultrasound (US) imaging, providing complementary contrast mechanisms. While PACT can quantify the brain’s oxygen saturation of hemoglobin (sO<inline-formula> <tex-math>$_{{2}}text {)}$ </tex-math></inline-formula>, US imaging can probe the blood flow based on the Doppler effect. Further, by tracking gas-filled microbubbles, ultrasound localization microscopy (ULM) can map the blood flow velocity with sub-diffraction spatial resolution. In this work, we present a 3D deep-brain imaging system that seamlessly integrates PACT and ULM into a single device, 3D-PAULM. Using a low ultrasound frequency of 4 MHz, 3D-PAULM is capable of imaging the brain hemodynamic functions with intact scalp and skull in a totally non-invasive manner. Using 3D-PAULM, we studied the mouse brain functions with ischemic stroke. Multi-spectral PACT, US B-mode imaging, microbubble-enhanced power Doppler (PD), and ULM were performed on the same mouse brain with intrinsic image co-registration. From the multi-modality measurements, we further quantified blood perfusion, sO2, vessel density, and flow velocity of the mouse brain, showing stroke-induced ischemia, hypoxia, and reduced blood flow. We expect that 3D-PAULM can find broad applications in studying deep brain functions on small animal models.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 2","pages":"994-1004"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GlandSAM: Injecting Morphology Knowledge Into Segment Anything Model for Label-Free Gland Segmentation GlandSAM：为无标签腺体分段模型注入形态学知识

IEEE transactions on medical imaging Pub Date : 2024-10-08 DOI: 10.1109/TMI.2024.3476176

Qixiang Zhang;Yi Li;Cheng Xue;Haonan Wang;Xiaomeng Li

{"title":"GlandSAM: Injecting Morphology Knowledge Into Segment Anything Model for Label-Free Gland Segmentation","authors":"Qixiang Zhang;Yi Li;Cheng Xue;Haonan Wang;Xiaomeng Li","doi":"10.1109/TMI.2024.3476176","DOIUrl":"10.1109/TMI.2024.3476176","url":null,"abstract":"This paper presents a label-free gland segmentation, GlandSAM, which achieves comparable performance with supervised methods while no label is required during its training or inference phase. We observe that the Segment Anything model produces sub-optimal results on gland dataset: It either over-segments a gland into many fractions or under-segments the gland regions by confusing many of them with the background, due to the complex morphology of glands and lack of sufficient labels. To address this challenge, our GlandSAM innovatively injects two clues about gland morphology into SAM to guide the segmentation process: (1) Heterogeneity within glands and (2) Similarity with the background. Initially, we leverage the clues to decompose the intricate glands by selectively extracting a proposal for each gland sub-region of heterogeneous appearances. Then, we inject the morphology clues into SAM in a fine-tuning manner with a novel morphology-aware semantic grouping module that explicitly groups the high-level semantics of gland sub-regions. In this way, our GlandSAM could capture comprehensive knowledge about gland morphology, and produce well-delineated and complete segmentation results. Extensive experiments conducted on the GlaS dataset and the CRAG dataset reveal that GlandSAM outperforms state-of-the-art label-free methods by a significant margin. Notably, our GlandSAM even surpasses several fully-supervised methods that require pixel-wise labels for training, which highlights the remarkable performance and potential of GlandSAM in the realm of gland segmentation.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 2","pages":"1070-1082"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142385483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unleash the Power of State Space Model for Whole Slide Image With Local Aware Scanning and Importance Resampling 利用局部感知扫描和重要性重采样，释放状态空间模型对整个幻灯片图像的处理能力

IEEE transactions on medical imaging Pub Date : 2024-10-07 DOI: 10.1109/TMI.2024.3475587

Yanyan Huang;Weiqin Zhao;Yu Fu;Lingting Zhu;Lequan Yu

{"title":"Unleash the Power of State Space Model for Whole Slide Image With Local Aware Scanning and Importance Resampling","authors":"Yanyan Huang;Weiqin Zhao;Yu Fu;Lingting Zhu;Lequan Yu","doi":"10.1109/TMI.2024.3475587","DOIUrl":"10.1109/TMI.2024.3475587","url":null,"abstract":"Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. However, previous methods often fall short of efficiently processing entire WSIs due to their gigapixel size. Inspired by recent developments in state space models, this paper introduces a new Pathology Mamba (PAM) for more accurate and robust WSI analysis. PAM includes three carefully designed components to tackle the challenges of enormous image size, the utilization of local and hierarchical information, and the mismatch between the feature distributions of training and testing during WSI analysis. Specifically, we design a Bi-directional Mamba Encoder to process the extensive patches present in WSIs effectively and efficiently, which can handle large-scale pathological images while achieving high performance and accuracy. To further harness the local information and inherent hierarchical structure of WSI, we introduce a novel Local-aware Scanning module, which employs a local-aware mechanism alongside hierarchical scanning to adeptly capture both the local information and the overarching structure within WSIs. Moreover, to alleviate the patch feature distribution misalignment between training and testing, we propose a Test-time Importance Resampling module to conduct testing patch resampling to ensure consistency of feature distribution between the training and testing phases, and thus enhance model prediction. Extensive evaluation on nine WSI datasets with cancer subtyping and survival prediction tasks demonstrates that PAM outperforms current state-of-the-art methods and also its enhanced capability in modeling discriminative areas within WSIs. The source code is available at <uri>https://github.com/HKU-MedAI/PAM</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 2","pages":"1032-1042"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0