Eric I-Chao Chang
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Explore the profile of Eric I-Chao Chang including associated specialties, affiliations and a list of published articles.
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29
Citations
614
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Recent Articles
1.
Li K, Qian Z, Han Y, Chang E, Wei B, Lai M, et al.
Med Image Anal
. 2023 Mar;
86:102791.
PMID: 36933385
Accurate segmentation in histopathology images at pixel-level plays a critical role in the digital pathology workflow. The development of weakly supervised methods for histopathology image segmentation liberates pathologists from time-consuming...
2.
Li Y, Qiu Z, Fan X, Liu X, Chang E, Xu Y
PLoS One
. 2022 Aug;
17(8):e0270339.
PMID: 35969596
MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation....
3.
Zhou Y, Yang Z, Zhang H, Chang E, Fan Y, Xu Y
IEEE Trans Med Imaging
. 2022 Mar;
41(8):2092-2104.
PMID: 35239478
Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great...
4.
Li Y, Cui J, Sheng Y, Liang X, Wang J, Chang E, et al.
Comput Med Imaging Graph
. 2021 Oct;
93:101991.
PMID: 34634548
Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions,...
5.
Deng S, Zhang X, Yan W, Chang E, Fan Y, Lai M, et al.
Front Med
. 2020 Jul;
14(4):470-487.
PMID: 32728875
Deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features....
6.
Borovec J, Kybic J, Arganda-Carreras I, Sorokin D, Bueno G, Khvostikov A, et al.
IEEE Trans Med Imaging
. 2020 Apr;
39(10):3042-3052.
PMID: 32275587
Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner....
7.
Yang Q, Li N, Zhao Z, Fan X, Chang E, Xu Y
Sci Rep
. 2020 Mar;
10(1):3753.
PMID: 32111966
We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images. Our proposed method performs Image Modality Translation (abbreviated as IMT) by means...
8.
Liu C, Fan X, Guo Z, Mo Z, Chang E, Xu Y
BMC Bioinformatics
. 2019 Dec;
20(1):724.
PMID: 31852433
Background: Quantitative areas is of great measurement of wound significance in clinical trials, wound pathological analysis, and daily patient care. 2D methods cannot solve the problems caused by human body...
9.
Zhao S, Lau T, Luo J, Chang E, Xu Y
IEEE J Biomed Health Inform
. 2019 Nov;
24(5):1394-1404.
PMID: 31689224
3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to...
10.
Wang Y, Fan X, Chen L, Chang E, Ananiadou S, Tsujii J, et al.
BMC Bioinformatics
. 2019 Aug;
20(1):430.
PMID: 31419946
*: Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are...