Zobrazeno 1 - 10
of 13 595
pro vyhledávání: '"A. Campanella"'
Autor:
Chen, Shengjia, Campanella, Gabriele, Elmas, Abdulkadir, Stock, Aryeh, Zeng, Jennifer, Polydorides, Alexandros D., Schoenfeld, Adam J., Huang, Kuan-lin, Houldsworth, Jane, Vanderbilt, Chad, Fuchs, Thomas J.
Recent advances in artificial intelligence (AI), in particular self-supervised learning of foundation models (FMs), are revolutionizing medical imaging and computational pathology (CPath). A constant challenge in the analysis of digital Whole Slide I
Externí odkaz:
http://arxiv.org/abs/2407.07841
Autor:
Campanella, Gabriele, Chen, Shengjia, Verma, Ruchika, Zeng, Jennifer, Stock, Aryeh, Croken, Matt, Veremis, Brandon, Elmas, Abdulkadir, Huang, Kuan-lin, Kwan, Ricky, Houldsworth, Jane, Schoenfeld, Adam J., Vanderbilt, Chad
The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months.
Externí odkaz:
http://arxiv.org/abs/2407.06508
We evaluate the quantum backreaction due to a gauge field coupled to a pseudo scalar field driving a slow-roll inflationary stage, the so-called axion inflation. The backreaction is evaluated for the first time using a gauge invariant approach, going
Externí odkaz:
http://arxiv.org/abs/2406.19960
Autor:
E. Tanzi, S. M Di Modica, J. Bordini, V. Olivari, M. Pettinato, A. Pagani, A. Campanella, L. Silvestri, A. Nai
Publikováno v:
HemaSphere, Vol 6, Pp 172-173 (2022)
Externí odkaz:
https://doaj.org/article/e7f4a8dc301d40e9ac7706b775df0cbc
Autor:
J. Bordini, C. Lenzi, L. Toscani, P. Ranghetti, E. Perotta, L. Scarfò, P. Ghia, A. Campanella
Publikováno v:
HemaSphere, Vol 6, Pp 496-497 (2022)
Externí odkaz:
https://doaj.org/article/1344ee45ac0140bba0e1760057cf5fad
Autor:
C. Lenzi, J. Bordini, A. Pseftogkas, A. Morabito, G. Tsiolas, F. E. Psomopoulos, A. Campanella, M. Frenquelli, P. Ghia
Publikováno v:
HemaSphere, Vol 6, Pp 515-516 (2022)
Externí odkaz:
https://doaj.org/article/835e6ba3506a4203970e54444b917717
Autor:
Campanella, Charlie, van der Goot, Rob
Large language models (LLMs) have emerged as a useful technology for job matching, for both candidates and employers. Job matching is often based on a particular geographic location, such as a city or region. However, LLMs have known biases, commonly
Externí odkaz:
http://arxiv.org/abs/2403.08046
Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling
Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets. Computational Pathology, with its massive amounts of microscopy image data and impact on diagnostics and biomarkers,
Externí odkaz:
http://arxiv.org/abs/2403.04865
Autor:
Campanella, Gabriele, Kwan, Ricky, Fluder, Eugene, Zeng, Jennifer, Stock, Aryeh, Veremis, Brandon, Polydorides, Alexandros D., Hedvat, Cyrus, Schoenfeld, Adam, Vanderbilt, Chad, Kovatch, Patricia, Cordon-Cardo, Carlos, Fuchs, Thomas J.
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks. While this training paradigm is well suited for the medical doma
Externí odkaz:
http://arxiv.org/abs/2310.07033
Publikováno v:
International Journal of Occupational Medicine and Environmental Health, Vol 37, Iss 3, Pp 244-256 (2024)
Green jobs are to be understood as those jobs directly associated with specific sustainability issues and activities related to the efficiency, quality and innovation of goods and services offered, from an eco-sustainability perspective. The objectiv
Externí odkaz:
https://doaj.org/article/78d8fb7d086042e8a1c5833e2bfaa166