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pro vyhledávání: '"Ambekar, A"'
Deep learning models in medical imaging often encounter challenges when adapting to new clinical settings unseen during training. Test-time adaptation offers a promising approach to optimize models for these unseen domains, yet its application in ano
Externí odkaz:
http://arxiv.org/abs/2410.03306
Autor:
Prakash, Anand, Ambekar, Sudhir
Publikováno v:
Higher Education, Skills and Work-Based Learning, 2024, Vol. 14, Issue 5, pp. 1149-1170.
Autor:
Walker, Cedric, Talawalla, Tasneem, Toth, Robert, Ambekar, Akhil, Rea, Kien, Chamian, Oswin, Fan, Fan, Berezowska, Sabina, Rottenberg, Sven, Madabhushi, Anant, Maillard, Marie, Barisoni, Laura, Horlings, Hugo Mark, Janowczyk, Andrew
The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorte
Externí odkaz:
http://arxiv.org/abs/2307.07528
Autor:
Ambekar, Rushikesh S., Bastos, Leonardo V., Galvao, Douglas S., Tiwary, Chandra S., Woellner, Cristiano F.
The topologically engineered complex Schwarzites architecture has been used to build novel and unique structural components with a high specific strength. The mechanical properties of these building blocks can be further tuned, reinforcing with stron
Externí odkaz:
http://arxiv.org/abs/2307.04540
This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. We follow the strict separation of source training and target testing, but exploit the value of the u
Externí odkaz:
http://arxiv.org/abs/2307.04033
Autor:
Bastos, Leonardo V., Ambekar, Rushikesh S., Tiwary, Chandra S., Galvao, Douglas S., Woellner, Cristiano F.
We carried out fully atomistic reactive molecular dynamics simulations to study the mechanical behavior of six newly proposed hybrid schwarzite-based structures (interlocked petal-schwarzites). Schwarzites are carbon crystalline nanostructures with n
Externí odkaz:
http://arxiv.org/abs/2307.02660
Publikováno v:
Indian Journal of Psychiatry, Vol 66, Iss 7, Pp 668-671 (2024)
Opioid prescriptions for chronic non-cancer pain raise concerns of addiction risks. Understanding the nuanced intersection of chronic pain and opioid use is crucial in clinical settings. We present four case studies from two tertiary care hospitals i
Externí odkaz:
https://doaj.org/article/23f8feef3eb64689992c4b8b21e081ed
Autor:
Cédric Walker, Tasneem Talawalla, Robert Toth, Akhil Ambekar, Kien Rea, Oswin Chamian, Fan Fan, Sabina Berezowska, Sven Rottenberg, Anant Madabhushi, Marie Maillard, Laura Barisoni, Hugo Mark Horlings, Andrew Janowczyk
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-7 (2024)
Abstract The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, P
Externí odkaz:
https://doaj.org/article/887f336880b34a1995344eabcbd97700
Autor:
Ambekar, Sameer, Tafuro, Matteo, Ankit, Ankit, van der Mast, Diego, Alence, Mark, Athanasiadis, Christos
With the usage of appropriate inductive biases, Counterfactual Generative Networks (CGNs) can generate novel images from random combinations of shape, texture, and background manifolds. These images can be utilized to train an invariant classifier, a
Externí odkaz:
http://arxiv.org/abs/2208.04226
Autor:
Akram, Mohammed Naveed, Ambekar, Akshatha, Sorokos, Ioannis, Aslansefat, Koorosh, Schneider, Daniel
Reliability estimation of Machine Learning (ML) models is becoming a crucial subject. This is particularly the case when such \mbox{models} are deployed in safety-critical applications, as the decisions based on model predictions can result in hazard
Externí odkaz:
http://arxiv.org/abs/2206.11116