Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Amir Safarpoor"'
Publikováno v:
Communications Biology, Vol 6, Iss 1, Pp 1-9 (2023)
tRNAsformer enables prediction of bulk RNA-seq from histological slides using machine learning approaches.
Externí odkaz:
https://doaj.org/article/5ef8071e367b4255bed8c7bc7d66e0f6
Autor:
Shivam Kalra, H. R. Tizhoosh, Sultaan Shah, Charles Choi, Savvas Damaskinos, Amir Safarpoor, Sobhan Shafiei, Morteza Babaie, Phedias Diamandis, Clinton J. V. Campbell, Liron Pantanowitz
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-15 (2020)
Abstract The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification a
Externí odkaz:
https://doaj.org/article/236179d0be3f4c138cb25ddc2e00fa92
Autor:
Hamid R. Tizhoosh, Morteza Babaie, Danial Maleki, Abtin Riasatian, Clinton J. V. Campbell, Phedias Diamandis, Shivam Kalra, Amir Safarpoor
Publikováno v:
The American Journal of Pathology. 191:1702-1708
One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imagin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0f8807a03c927db662d64660830d1dd
https://doi.org/10.1016/j.ajpath.2021.01.015
https://doi.org/10.1016/j.ajpath.2021.01.015
Publikováno v:
IEEE Transactions on Medical Imaging. 39:3355-3366
The colorless biopsied tissue samples are usually stained in order to visualize different microscopic structures for diagnostic purposes. But color variations associated with the process of sample preparation, usage of raw materials, diverse staining
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e35259343f83623c98473283625db5c
https://doi.org/10.1109/tmi.2020.2992108
https://doi.org/10.1109/tmi.2020.2992108
Autor:
Amir Safarpoor, Morteza Babaie, Savvas Damaskinos, Clinton J. V. Campbell, Phedias Diamandis, Shivam Kalra, Charles Choi, Liron Pantanowitz, Hamid R. Tizhoosh, Sobhan Shafiei, Sultaan Shah
Publikováno v:
npj Digital Medicine, Vol 3, Iss 1, Pp 1-15 (2020)
NPJ Digital Medicine
NPJ Digital Medicine
The emergence of digital pathology has opened new horizons for histopathology and cytology. Artificial-intelligence algorithms are able to operate on digitized slides to assist pathologists with diagnostic tasks. Whereas machine learning involving cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01feaf04bcaaba6470f911a3797c1dd5
http://link.springer.com/article/10.1038/s41746-020-0238-2
http://link.springer.com/article/10.1038/s41746-020-0238-2
Background: The size of whole slide images (WSIs) in digital pathology can vary from millions to billions of pixels. Accordingly, training state-of-the-art deep learning models with WSIs may not be feasible due to existing memory and computational co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7a209572a733ad9d61752c66c154e70
https://doi.org/10.21203/rs.3.rs-971708/v1
https://doi.org/10.21203/rs.3.rs-971708/v1
Autor:
Hamid R, Tizhoosh, Phedias, Diamandis, Clinton J V, Campbell, Amir, Safarpoor, Shivam, Kalra, Danial, Maleki, Abtin, Riasatian, Morteza, Babaie
Publikováno v:
The American journal of pathology. 191(10)
One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imagin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::0322d26c16192f6116a26c0679ae2e59
https://pubmed.ncbi.nlm.nih.gov/33636179
https://pubmed.ncbi.nlm.nih.gov/33636179
Publikováno v:
The Journal of pathologyReferences. 253(2)
Within artificial intelligence and machine learning, a generative model is a powerful tool for learning any kind of data distribution. With the advent of deep learning and its success in image recognition, the field of deep generative models has clea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bde56c88447559473e2f0177b3a0da37
https://pubmed.ncbi.nlm.nih.gov/32686118
https://pubmed.ncbi.nlm.nih.gov/32686118
Autor:
Benyamin Ghojogh, Fakhri Karray, Milad Sikaroudi, Hamid R. Tizhoosh, Amir Safarpoor, Mark Crowley
Publikováno v:
Advances in Visual Computing ISBN: 9783030645557
ISVC (1)
ISVC (1)
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, both in online and offline mining
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4d034c179743c6fc6d4d79fe49ede2d
https://doi.org/10.1007/978-3-030-64556-4_26
https://doi.org/10.1007/978-3-030-64556-4_26
Autor:
Mark Crowley, Benyamin Ghojogh, Hamid R. Tizhoosh, Amir Safarpoor, Milad Sikaroudi, Sobhan Shafiei
Publikováno v:
EMBC
As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75d20f98796c39d133639b7c3ea4daa0