Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Liao, Chien-Hung"'
Autor:
Wang, Fakai, Cheng, Chi-Tung, Peng, Chien-Wei, Yan, Ke, Wu, Min, Lu, Le, Liao, Chien-Hung, Zhang, Ling
Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the pot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c4ea0137af2010047a3b83f20b63fe1
Purpose Automated detection of region of interest (ROI) is a critical step for many medical image applications such as heart ROIs detection in perfusion MRI images, lung boundary detection in chest X-rays, and femoral head detection in pelvic radiogr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0a1d8c5c013379496440ed8755fee1a
http://arxiv.org/abs/2103.01584
http://arxiv.org/abs/2103.01584
Autor:
Cheng, Chi-Tung, Cai, Jinzheng, Teng, Wei, Zheng, Youjing, Huang, YuTing, Wang, Yu-Chao, Peng, Chien-Wei, Tang, Youbao, Lee, Wei-Chen, Yeh, Ta-Sen, Xiao, Jing, Lu, Le, Liao, Chien-Hung, Harrison, Adam P.
Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies under varied clinical scenarios, e.g., fully dynamic contrast enhanced (DCE) studies, non-contrast (NC) plus venous phase (VP) abdominal stud
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7735d22e3780b4faa6f459bc20a9b13b
Autor:
Zhang, Xinyu, Wang, Yirui, Cheng, Chi-Tung, Lu, Le, Harrison, Adam P., Xiao, Jing, Liao, Chien-Hung, Miao, Shun
Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c444885a47cf93001615dfcedd6cee3
http://arxiv.org/abs/2012.04066
http://arxiv.org/abs/2012.04066
Autor:
Cai, Jinzheng, Yan, Ke, Cheng, Chi-Tung, Xiao, Jing, Liao, Chien-Hung, Lu, Le, Harrison, Adam P.
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/significant-findings detection techniques are at the core of medical i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fa625281cada322ed22124d933e227d
http://arxiv.org/abs/2008.13254
http://arxiv.org/abs/2008.13254
Autor:
Wang, Yirui, Zheng, Kang, Chang, Chi-Tung, Zhou, Xiao-Yun, Zheng, Zhilin, Huang, Lingyun, Xiao, Jing, Lu, Le, Liao, Chien-Hung, Miao, Shun
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohibitively high labor costs involved in large-scale medical image annotat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afef46a149b81fc652481b506d7230a0
Autor:
Huo, Yuankai, Cai, Jinzheng, Cheng, Chi-Tung, Raju, Ashwin, Yan, Ke, Landman, Bennett A., Xiao, Jing, Lu, Le, Liao, Chien-Hung, Harrison, Adam P.
Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a specific population of human subj
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8f53f993eab0a1e26c666f763235fc5
Autor:
Chen, Haomin, Wang, Yirui, Zheng, Kang, Li, Weijian, Cheng, Chi-Tung, Harrison, Adam P., Xiao, Jing, Hager, Gregory D., Lu, Le, Liao, Chien-Hung, Miao, Shun
Visual cues of enforcing bilaterally symmetric anatomies as normal findings are widely used in clinical practice to disambiguate subtle abnormalities from medical images. So far, inadequate research attention has been received on effectively emulatin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f085414609b7643cf815608e7d60fb8b
Autor:
Zhou, Bo, Harrison, Adam P., Yao, Jiawen, Cheng, Chi-Tung, Xiao, Jing, Liao, Chien-Hung, Lu, Le
As the demand for more descriptive machine learning models grows within medical imaging, bottlenecks due to data paucity will exacerbate. Thus, collecting enough large-scale data will require automated tools to harvest data/label pairs from messy and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88d9347d16d245f78985aa0e1ae1a4bf
http://arxiv.org/abs/1909.02511
http://arxiv.org/abs/1909.02511
Autor:
Wang, Yirui, Lu, Le, Cheng, Chi-Tung, Jin, Dakai, Harrison, Adam P., Xiao, Jing, Liao, Chien-Hung, Miao, Shun
Hip and pelvic fractures are serious injuries with life-threatening complications. However, diagnostic errors of fractures in pelvic X-rays (PXRs) are very common, driving the demand for computer-aided diagnosis (CAD) solutions. A major challenge lie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c12986c08ad4190debd6670a2253b3b
http://arxiv.org/abs/1909.02077
http://arxiv.org/abs/1909.02077