4篇
您的检索式：作者名="Jinman Kim"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A Review of Predictive and Contrastive Self-supervised Learning for Medical Images显示文摘Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain. | Wei-Chien Wang Euijoon Ahn Dagan Feng Jinman Kim | 2023 | Machine Intelligence Research2023，20，4： | 2 |
| 2 | The effects of Ginkgo biloba extract (GBe) on axonal transport,microvasculature and morphology of sciatic nerve in streptozotocin-induced diabetic rats显示文摘 | Jinman Kim Kazuhito Yokoyama Shunichi Araki | 2000 | Environ Health Prev Med2000，5，2： | 1 |
| 3 | Importance-aware 3D volume visualization for medical content-based image retrieval-a preliminary study显示文摘Background A medical content-based image retrieval(CBIR)system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image.CBIR is widely used in evidence-based diagnosis,teaching,and research.Although the retrieval accuracy has largely improved,there has been limited development toward visualizing important image features that indicate the similarity of retrieved images.Despite the prevalence of 3D volumetric data in medical imaging such as computed tomography(CT),current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images.Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information,including the size,shape,and spatial relations of multiple structures.This process is time-consuming and reliant on users'experience.Methods In this study,we proposed an importance-aware 3D volume visualization method.The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process.We then integrated the proposed visualization into a CBIR system,thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.Results Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography(PETCT)images of a non-small cell lung cancer dataset. | Mingjian LI Younhyun JUNG Michael FULHAM Jinman KIM | 2024 | 虚拟现实与智能硬件(中英文)2024，6，1： | 0 |
| 4 | Computer graphics for metaverse显示文摘CGI is one of the oldest international conferences in Computer Graphics in the world.It is the official conference of the Computer Graphics Society(CGS),a long-standing international computer graphics organization.CGI conference has been held annually in many different countries across the world and has gained a reputation as one of the key conferences for researchers and practitioners to share their achievements and discover the latest advances in Computer Graphics. | Nadia Magnenat THALMANN Jinman KIM George PAPAGIANNAKIS Daniel THALMANN Bin SHENG | 2022 | Virtual Reality & Intelligent Hardware2022，4，5： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员