Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Nima Tajbakhsh"'
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2010 (2010)
This work addresses the increasing demand for a sensitive and user-friendly iris based authentication system. We aim at reducing False Rejection Rate (FRR). The primary source of high FRR is the presence of degradation factors in iris texture. To red
Externí odkaz:
https://doaj.org/article/f25253307440485a891c122ae8a87eee
Publikováno v:
IEEE Trans Med Imaging
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive archite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8b81b0cfa22d967709725dbd441e10f
https://doi.org/10.1109/tmi.2019.2959609
https://doi.org/10.1109/tmi.2019.2959609
Publikováno v:
IEEE Trans Med Imaging
Annotation-efficient deep learning refers to methods and practices that yield high-performance deep learning models without the use of massive carefully labeled training datasets. This paradigm has recently attracted attention from the medical imagin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c07163d729eb71d150510c0f80ba60d
https://pubmed.ncbi.nlm.nih.gov/34795461
https://pubmed.ncbi.nlm.nih.gov/34795461
Publikováno v:
ISBI
Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data. Few-shot segmentation approaches address this issue by learning to transfer knowledge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53ddc8973ee43987e13fc9e5e9517fb1
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597092
MICCAI (1)
MICCAI (1)
Supervised learning has proved effective for medical image analysis. However, it can utilize only the small labeled portion of data; it fails to leverage the large amounts of unlabeled data that is often available in medical image datasets. Supervise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6eb946b016d5ac5c884385329241a522
https://doi.org/10.1007/978-3-030-59710-8_68
https://doi.org/10.1007/978-3-030-59710-8_68
Autor:
Nima Tajbakhsh, Kenji Suzuki
Publikováno v:
Pattern Recognition. 63:476-486
End-to-end learning machines enable a direct mapping from the raw input data to the desired outputs, eliminating the need for hand-crafted features. Despite less engineering effort than the hand-crafted counterparts, these learning machines achieve e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de8877302deef34f93b0e9a88b0051f3
http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100828351
http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100828351
Publikováno v:
Medical image analysis. 63
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5d2bc9edea3beccb3943163e80d9609
https://pubmed.ncbi.nlm.nih.gov/32289663
https://pubmed.ncbi.nlm.nih.gov/32289663
Autor:
Nima Tajbakhsh, Mahfuzur Rahman Siddiquee, Michael B. Gotway, Yoshua Bengio, Zongwei Zhou, Ruibin Feng, Jianming Liang
Publikováno v:
ICCV
Proc IEEE Int Conf Comput Vis
Proc IEEE Int Conf Comput Vis
Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while other
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fb2a070ed2aaa1363f137b32d8bf6d7
http://arxiv.org/abs/1908.06965
http://arxiv.org/abs/1908.06965
Autor:
Vatsal Sodha, Michael B. Gotway, Ruibin Feng, Zongwei Zhou, Nima Tajbakhsh, Mahfuzur Rahman Siddiquee, Jianming Liang
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030322502
MICCAI (4)
Med Image Comput Comput Assist Interv
MICCAI (4)
Med Image Comput Comput Assist Interv
Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g.,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e8c1c6a41bd2567a3746a2f34edc78d
https://doi.org/10.1007/978-3-030-32251-9_42
https://doi.org/10.1007/978-3-030-32251-9_42
Publikováno v:
ISBI
Deep convolutional neural networks have proved effective in segmenting lesions and anatomies in various medical imaging modalities. However, in the presence of small sample size and domain shift problems, these models often produce masks with non-int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c93c275b8160e3aff4d84dc924464dfe