Zobrazeno 1 - 10
of 444
pro vyhledávání: '"C. Rajapakse"'
Autor:
Jocelyn Hui Lin Goh, BEng, Elroy Ang, BEng, Sahana Srinivasan, BEng, Xiaofeng Lei, MSc, Johnathan Loh, MEng, Ten Cheer Quek, BEng, Cancan Xue, PhD, Xinxing Xu, PhD, Yong Liu, PhD, Ching-Yu Cheng, PhD, Jagath C. Rajapakse, PhD, Yih-Chung Tham, PhD
Publikováno v:
Ophthalmology Science, Vol 4, Iss 6, Pp 100552- (2024)
Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is
Externí odkaz:
https://doaj.org/article/bd1577fcd2fe40daa1d4933b9d3ca3ef
Autor:
Charlene Z. L. Ong, N. Jannah M. Nasir, Roy E. Welsch, Lisa Tucker-Kellogg, Jagath C. Rajapakse
Publikováno v:
Frontiers in Bioinformatics, Vol 4 (2024)
BackgroundMicroscopy of regenerated tissue shows different morphologies between the healing of acute wounds and chronic wounds. This difference can be seen manually by biologists, but computational methods are needed to automate the characterization
Externí odkaz:
https://doaj.org/article/bf7b1daaa11a49728016e76cd39277a9
Autor:
Jagath C. Rajapakse, Chun Hung How, Yi Hao Chan, Luke Chin Peng Hao, Abhinandan Padhi, Vivek Adrakatti, Iram Rais Alam Khan, Tchoyoson Lim
Publikováno v:
IEEE Access, Vol 12, Pp 60839-60848 (2024)
Intracranial hemorrhage (ICH) is an emergency and a potentially life-threatening condition. Automated segmentation of ICH from head CT images can provide clinicians with volumetric measures that can be used for diagnosis and decision support for trea
Externí odkaz:
https://doaj.org/article/c8ed2b8150ce47febcaaf0863728624a
Publikováno v:
IEEE Journal of Translational Engineering in Health and Medicine, Vol 12, Pp 371-381 (2024)
Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimagin
Externí odkaz:
https://doaj.org/article/963ac69025d44bc3b11bb2cce3aa96a6
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC) features have been observed to vary across study sites, warranting the need for methods that can differentiate between site-invariant FC biomarkers and
Externí odkaz:
https://doaj.org/article/99d8226f26414d99ba915f81e797356e
Publikováno v:
Heliyon, Vol 9, Iss 12, Pp e22412- (2023)
A supervised deep learning network like the UNet has performed well in segmenting brain anomalies such as lesions and tumours. However, such methods were proposed to perform on single-modality or multi-modality images. We use the Hybrid UNet Transfor
Externí odkaz:
https://doaj.org/article/c46d12ec6fdd4abcb154e939cbdbfeed
Autor:
Wei Kwek Soh, Jagath C. Rajapakse
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
A hybrid UNet and Transformer (HUT) network is introduced to combine the merits of the UNet and Transformer architectures, improving brain lesion segmentation from MRI and CT scans. The HUT overcomes the limitations of conventional approaches by util
Externí odkaz:
https://doaj.org/article/6de0f87a16b14c71bf6838f084342039
Publikováno v:
BMC Bioinformatics, Vol 22, Iss S10, Pp 1-15 (2022)
Abstract Background Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient’s responses to numerous cancer drugs are needed for personaliz
Externí odkaz:
https://doaj.org/article/8b790a74b5764e87985dbd09d798f78c
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis.
Externí odkaz:
https://doaj.org/article/60e59149d414458bb3a97d473f28e7cc
Autor:
M G C Sooriyabandara, J M S M Jayasundara, M S L R P Marasinghe, H A B M Hathurusinghe, A U Bandaranayake, K A N C Jayawardane, R M R Nilanthi, R C Rajapakse, P C G Bandaranayake
Publikováno v:
PLoS ONE, Vol 18, Iss 6, p e0285572 (2023)
Elephas maximus maximus Linnaeus, the Sri Lankan subspecies is the largest and the darkest among Asian elephants. Patches of depigmented areas with no skin color on the ears, face, trunk, and belly morphologically differentiate it from the others. Th
Externí odkaz:
https://doaj.org/article/b2ce556bd3b34ad58fb1100a7f9f96dc