2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)最新文献_第2页

Self Supervised Lesion Recognition for Breast Ultrasound Diagnosis 乳腺超声诊断的自我监督病灶识别

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761701

Yuanfan Guo, Canqian Yang, Tiancheng Lin, Chunxiao Li, Rui Zhang, Yi Xu

{"title":"Self Supervised Lesion Recognition for Breast Ultrasound Diagnosis","authors":"Yuanfan Guo, Canqian Yang, Tiancheng Lin, Chunxiao Li, Rui Zhang, Yi Xu","doi":"10.1109/ISBI52829.2022.9761701","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761701","url":null,"abstract":"Previous deep learning based Computer Aided Diagnosis (CAD) system treats multiple views of the same lesion as independent images. Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic relationship between different views of a lesion, which is inconsistent with the traditional diagnosis where sonographers analyze a lesion from at least two views. In this paper, we propose a multi-task framework that complements Benign/Malignant classification task with lesion recognition (LR) which helps leveraging relationship among multiple views of a single lesion to learn a complete representation of the lesion. To be specific, LR task employs contrastive learning to encourage representation that pulls multiple views of the same lesion and repels those of different lesions. The task therefore facilitates a representation that is not only invariant to the view change of the lesion, but also capturing fine-grained features to distinguish between different lesions. Experiments show that the proposed multi-task framework boosts the performance of Benign/Malignant classification as two sub-tasks complement each other and enhance the learned representation of ultrasound images.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"182 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85449260","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: 2

Breathing-Compensated Neural Networks for Real Time C-Arm Pose Estimation in Lung CT-Fluoroscopy Registration 肺ct透视登记中实时c臂姿态估计的呼吸补偿神经网络

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761705

Brian C. Lee, Ayushi Sinha, N. Varble, W. Pritchard, J. Karanian, B. Wood, T. Bydlon

{"title":"Breathing-Compensated Neural Networks for Real Time C-Arm Pose Estimation in Lung CT-Fluoroscopy Registration","authors":"Brian C. Lee, Ayushi Sinha, N. Varble, W. Pritchard, J. Karanian, B. Wood, T. Bydlon","doi":"10.1109/ISBI52829.2022.9761705","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761705","url":null,"abstract":"Augmentation of interventional c-arm fluoroscopy using information extracted from pre-operative imaging has the potential to reduce procedure times and improve patient outcomes in minimally invasive peripheral lung procedures, where breathing motion, small airways, and anatomical variation create a challenging environment for planned pathway navigation. Extraction of the rigid c-arm pose relative to preoperative images is a crucial prerequisite; however, accurate 2D-3D fluoroscopy-CT soft tissue registration in the presence of natural deformable patient motion remains challenging. We propose to train a patient-specific neural network on synthetic fluoroscopy derived from the patient’s pre-operative CT, augmented by a generalized breathing motion model, to predict c-arm pose. Our model includes an image supervision path that infers the x-ray projection geometry, providing training stability across patients. We train our model on synthetic fluoroscopy generated from preclinical swine CT and we evaluate on synthetic and real fluoroscopy.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"25 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72852165","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: 1

Deep Neural Network for Combined Particle Tracking and Colocalization Analysis in Two-Channel Microscopy Images 基于深度神经网络的双通道显微图像粒子跟踪与共定位分析

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761696

Roman Spilger, Ji Young Lee, R. Bartenschlager, K. Rohr

引用次数: 1

Data Driven Estimation of Covid-19 Prognosis 数据驱动的Covid-19预后评估

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761406

Harshit Sharma, R. Nagar, Deepak Mishra

引用次数: 1

Spatial Position Estimation Method for 3D Ultrasound Reconstruction Based on Hybrid Transfomers 基于混合变压器的三维超声重构空间位置估计方法

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761499

Guochen Ning, Hanying Liang, Lei Zhou, Xinran Zhang, Hongen Liao

引用次数: 4

Multi-Tasking DSSD Architecture for Laparoscopic Cholecystectomy Surgical Assistance Systems 腹腔镜胆囊切除术手术辅助系统的多任务DSSD架构

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761562

Chakka Sai Pradeep, N. Sinha

引用次数: 1

Prediction of Cognitive Scores by Movie-Watching FMRI Connectivity and Eye Movement Via Spectral Graph Convolutions 通过谱图卷积预测看电影的FMRI连通性和眼动的认知得分

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761565

Jiaxing Gao, Changhe Li, Zhibin He, Yaonai Wei, Lei Guo, Junwei Han, Shenmin Zhang, Tuo Zhang

{"title":"Prediction of Cognitive Scores by Movie-Watching FMRI Connectivity and Eye Movement Via Spectral Graph Convolutions","authors":"Jiaxing Gao, Changhe Li, Zhibin He, Yaonai Wei, Lei Guo, Junwei Han, Shenmin Zhang, Tuo Zhang","doi":"10.1109/ISBI52829.2022.9761565","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761565","url":null,"abstract":"Brain functional connectivity has been demonstrated to serve as a \"fingerprint\" to predict individual behaviors and phenotypes. A precise mapping between them could provide insightful clues to brain architectures and the generation of cognition. In this context, the naturalistic paradigm provides more engaging conditions and richer fMRI information, and both preserves or even enhances individual features and increases sensitivity to phenotypic measures, compared with other functional MRI modalities including resting-state and task paradigms. However, to the best of our knowledge, only linear methods were developed for predicting phenotypic measures from brain activity under naturalistic stimulus, while the brain activity is highly dynamic and nonlinear. Hence, we adopted the nonlinear graph convolutional network (GCN) to predict cognition-related phenotypic score from brain functional connectivity under naturalistic stimulus, where subjects are the nodes and functional connectivity is node feature. The behavior patterns of eye movement were integrated into this method to estimate similarity across subjects and define the graph edges. A few nodes are labeled by their phenotypic score, and the model is trained to predict the scores of those unlabeled nodes. The prediction accuracy of this method outperforms those from the linear classification method, resting-state based functional node feature and random edge tests.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"39 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86362635","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

MC-PDNet: Deep Unrolled Neural Network For Multi-Contrast Mr Image Reconstruction From Undersampled K-Space Data 基于欠采样k空间数据的多对比度Mr图像重建的深度展开神经网络

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761583

K. Pooja, Zaccharie Ramzi, G. R. Chaithya, P. Ciuciu

引用次数: 3

Class-Based Attention Mechanism for Chest Radiograph Multi-Label Categorization 基于类别的胸片多标签分类注意机制

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761667

David Sriker, H. Greenspan, J. Goldberger

引用次数: 1

Towards Generalization of Medical Imaging AI Models: Sharpness-Aware Minimizers and Beyond 医学成像人工智能模型的推广:清晰度感知最小化及超越

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) Pub Date : 2022-03-28 DOI: 10.1109/ISBI52829.2022.9761677

Deepak Anand, Rohan Patil, Utkarsh Agrawal, Rahul Venkataramani, Hariharan Ravishankar, Prasad Sudhakar

{"title":"Towards Generalization of Medical Imaging AI Models: Sharpness-Aware Minimizers and Beyond","authors":"Deepak Anand, Rohan Patil, Utkarsh Agrawal, Rahul Venkataramani, Hariharan Ravishankar, Prasad Sudhakar","doi":"10.1109/ISBI52829.2022.9761677","DOIUrl":"https://doi.org/10.1109/ISBI52829.2022.9761677","url":null,"abstract":"AI models have become ubiquitous tools of choice for different medical imaging problems like enhancement, work-flow acceleration, etc.. While availability of large amounts of diverse data and reliable annotations continue to be a challenge, development cycles of these models have shrunk. This necessitates a reliable recipe for improving generalization of AI models that fare well during deployment on unseen data. In this paper, we investigate generalization through the lens of sharpness-aware optimizers. We study two representative problems in medical imaging: (a) a difficult task of cardiac view classification on ultrasound images and (b) COVID-19 detection from chest X-ray images and demonstrate high efficacy of flat minima solutions. Further, we perform extensive Hessian analysis that reveals the impact of the geometry of loss landscape towards generalization. Our empirical studies suggest that sharpness aware minimization improves generalization by 5−10%, over and above the gain obtained by other methods - on both in-domain and out-of-domain test data.","PeriodicalId":6827,"journal":{"name":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90598792","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: 1