Proceedings - International Symposium on Biomedical Imaging This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

HNAS-Reg: Hierarchical Neural Architecture Search for Deformable Medical Image Registration
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230534. doi: 10.1109/isbi53787.2023.10230534. Epub 2023 Sep 1.ABSTRACTConvolutional neural networks (CNNs) have been widely used to build deep learning models for medical image registration, but manually designed network architectures are not necessarily optimal. This paper presents a hierarchical NAS framework (HNAS-Reg), consisting of both convolutional operation search and network topology search, to identify the optimal network architecture for deformable medical image registration. To mitigate the computational overhead and memory constraints, a...
Source: Proceedings - International Symposium on Biomedical Imaging - October 4, 2023 Category: Radiology Authors: Jiong Wu Yong Fan Source Type: research

< em > Surf < /em > NN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230488. doi: 10.1109/isbi53787.2023.10230488. Epub 2023 Sep 1.ABSTRACTTo achieve fast, robust, and accurate reconstruction of the human cortical surfaces from 3D magnetic resonance images (MRIs), we develop a novel deep learning-based framework, referred to as SurfNN, to reconstruct simultaneously both inner (between white matter and gray matter) and outer (pial) surfaces from MRIs. Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependenc...
Source: Proceedings - International Symposium on Biomedical Imaging - October 4, 2023 Category: Radiology Authors: Hao Zheng Hongming Li Yong Fan Source Type: research

Optimal transport guided unsupervised learning for enhancing low-quality retinal images
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230719. doi: 10.1109/isbi53787.2023.10230719. Epub 2023 Sep 1.ABSTRACTReal-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections, and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result in inaccuracy or ambiguity in clinical diagnoses. In this paper, we proposed a simple but effective end-to-end framework for enhancing poor-quality retinal fundus images. Leveraging the optimal transport theory, we proposed an unpaired image-to-image translation scheme for transporting low-quality images...
Source: Proceedings - International Symposium on Biomedical Imaging - September 22, 2023 Category: Radiology Authors: Wenhui Zhu Peijie Qiu Mohammad Farazi Keshav Nandakumar Oana M Dumitrascu Yalin Wang Source Type: research

Optimal transport guided unsupervised learning for enhancing low-quality retinal images
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023. doi: 10.1109/isbi53787.2023.10230719. Epub 2023 Sep 1.ABSTRACTReal-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections, and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result in inaccuracy or ambiguity in clinical diagnoses. In this paper, we proposed a simple but effective end-to-end framework for enhancing poor-quality retinal fundus images. Leveraging the optimal transport theory, we proposed an unpaired image-to-image translation scheme for transporting low-quality images to their high-quality counterpa...
Source: Proceedings - International Symposium on Biomedical Imaging - September 22, 2023 Category: Radiology Authors: Wenhui Zhu Peijie Qiu Mohammad Farazi Keshav Nandakumar Oana M Dumitrascu Yalin Wang Source Type: research

Self-supervised learning with radiology reports, a comparative analysis of strategies for large vessel occlusion and brain cta images
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023:10.1109/isbi53787.2023.10230623. doi: 10.1109/isbi53787.2023.10230623. Epub 2023 Sep 1.ABSTRACTScarcity of labels for medical images is a significant barrier for training representation learning approaches based on deep neural networks. This limitation is also present when using imaging data collected during routine clinical care stored in picture archiving communication systems (PACS), as these data rarely have attached the high-quality labels required for medical image computing tasks. However, medical images extracted from PACS are commonly coupled with descriptive radiol...
Source: Proceedings - International Symposium on Biomedical Imaging - September 15, 2023 Category: Radiology Authors: S Pachade S Datta Y Dong S Salazar-Marioni R Abdelkhaleq A Niktabe K Roberts S A Sheth L Giancardo Source Type: research