Zobrazeno 1 - 10
of 101
pro vyhledávání: '"GALEOTTI, JOHN"'
Autor:
Gare, Gautam, Armouti, Jana, Madaan, Nikhil, Panda, Rohan, Fox, Tom, Hutchins, Laura, Krishnan, Amita, Rodriguez, Ricardo, DeBoisblanc, Bennett, Ramanan, Deva, Galeotti, John
A crucial question in active patient care is determining if a treatment is having the desired effect, especially when changes are subtle over short periods. We propose using inter-patient data to train models that can learn to detect these fine-grain
Externí odkaz:
http://arxiv.org/abs/2411.01144
Autor:
Gare, Gautam Rajendrakumar, Fox, Tom, Chansangavej, Beam, Krishnan, Amita, Rodriguez, Ricardo Luis, deBoisblanc, Bennett P, Ramanan, Deva Kannan, Galeotti, John Michael
Accurate and interpretable diagnostic models are crucial in the safety-critical field of medicine. We investigate the interpretability of our proposed biomarker-based lung ultrasound diagnostic pipeline to enhance clinicians' diagnostic capabilities.
Externí odkaz:
http://arxiv.org/abs/2402.12394
Autor:
Goel, Raghavv, Morales, Cecilia, Singh, Manpreet, Dubrawski, Artur, Galeotti, John, Choset, Howie
Segmenting a moving needle in ultrasound images is challenging due to the presence of artifacts, noise, and needle occlusion. This task becomes even more demanding in scenarios where data availability is limited. In this paper, we present a novel app
Externí odkaz:
http://arxiv.org/abs/2312.01239
In this paper, we present a novel deep-learning model for deformable registration of ultrasound images and an unsupervised approach to training this model. Our network employs recurrent all-pairs field transforms (RAFT) and a spatial transformer netw
Externí odkaz:
http://arxiv.org/abs/2306.13332
This paper presents a deep-learning model for deformable registration of ultrasound images at online rates, which we call U-RAFT. As its name suggests, U-RAFT is based on RAFT, a convolutional neural network for estimating optical flow. U-RAFT, howev
Externí odkaz:
http://arxiv.org/abs/2306.13329
Autor:
Gare, Gautam Rajendrakumar, Fox, Tom, Lowery, Pete, Zamora, Kevin, Tran, Hai V., Hutchins, Laura, Montgomery, David, Krishnan, Amita, Ramanan, Deva Kannan, Rodriguez, Ricardo Luis, deBoisblanc, Bennett P, Galeotti, John Michael
Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific
Externí odkaz:
http://arxiv.org/abs/2206.08398
Autor:
Gare, Gautam Rajendrakumar, Schoenling, Andrew, Philip, Vipin, Tran, Hai V, deBoisblanc, Bennett P, Rodriguez, Ricardo Luis, Galeotti, John Michael
Publikováno v:
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021, pp. 1406-1410
We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse-transfer learning. We present an architecture to convert segmentation
Externí odkaz:
http://arxiv.org/abs/2201.10166
Autor:
Gare, Gautam Rajendrakumar, Chen, Wanwen, Hung, Alex Ling Yu, Chen, Edward, Tran, Hai V., Fox, Tom, Lowery, Pete, Zamora, Kevin, deBoisblanc, Bennett P, Rodriguez, Ricardo Luis, Galeotti, John Michael
Publikováno v:
LL-COVID19 2021. Lecture Notes in Computer Science, vol 12969. Springer, Cham
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveal
Externí odkaz:
http://arxiv.org/abs/2201.07368
Autor:
Gare, Gautam Rajendrakumar, Tran, Hai V., deBoisblanc, Bennett P, Rodriguez, Ricardo Luis, Galeotti, John Michael
With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analys
Externí odkaz:
http://arxiv.org/abs/2201.07357
Autor:
Zevallos, Nico, Harber, Evan, Abhimanyu, Patel, Kirtan, Gu, Yizhu, Sladick, Kenny, Guyette, Francis, Weiss, Leonard, Pinsky, Michael R., Gomez, Hernando, Galeotti, John, Choset, Howie
Advanced resuscitative technologies, such as Extra Corporeal Membrane Oxygenation (ECMO) cannulation or Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA), are technically difficult even for skilled medical personnel. This paper descri
Externí odkaz:
http://arxiv.org/abs/2107.02839