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pro vyhledávání: '"Lo, Joseph Y"'
Autor:
Yang, Julia, Barnett, Alina Jade, Donnelly, Jon, Kishore, Satvik, Fang, Jerry, Schwartz, Fides Regina, Chen, Chaofan, Lo, Joseph Y., Rudin, Cynthia
Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models
Externí odkaz:
http://arxiv.org/abs/2406.06386
Autor:
Dahal, Lavsen, Ghojoghnejad, Mobina, Ghosh, Dhrubajyoti, Bhandari, Yubraj, Kim, David, Ho, Fong Chi, Tushar, Fakrul Islam, Luoa, Sheng, Lafata, Kyle J., Abadi, Ehsan, Samei, Ehsan, Lo, Joseph Y., Segars, W. Paul
Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current librarie
Externí odkaz:
http://arxiv.org/abs/2405.11133
Autor:
Tushar, Fakrul Islam, Wang, Avivah, Dahal, Lavsen, Harowicz, Michael R., Lafata, Kyle J., Tailor, Tina D., Lo, Joseph Y.
Lung cancer's high mortality rate can be mitigated by early detection, increasingly reliant on AI for diagnostic imaging. However, AI model performance depends on training and validation datasets. This study develops and validates AI models for both
Externí odkaz:
http://arxiv.org/abs/2405.04605
Autor:
Tushar, Fakrul Islam, Vancoillie, Liesbeth, McCabe, Cindy, Kavuri, Amareswararao, Dahal, Lavsen, Harrawood, Brian, Fryling, Milo, Zarei, Mojtaba, Sotoudeh-Paima, Saman, Ho, Fong Chi, Ghosh, Dhrubajyoti, Luo, Sheng, Segars, W. Paul, Abadi, Ehsan, Lafata, Kyle J., Samei, Ehsan, Lo, Joseph Y.
Importance: Clinical imaging trials are crucial for definitive evaluation of medical innovations, but the process is inefficient, expensive, and ethically-constrained. Virtual imaging trial (VIT) approach address these limitations by emulating the co
Externí odkaz:
http://arxiv.org/abs/2404.11221
Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about the limitations of such weakly supervised learning and the effect of th
Externí odkaz:
http://arxiv.org/abs/2402.04419
Autor:
Mouheb, Kaouther, Nejad, Mobina Ghojogh, Dahal, Lavsen, Samei, Ehsan, Lafata, Kyle J., Segars, W. Paul, Lo, Joseph Y.
Accurate 3D modeling of human organs plays a crucial role in building computational phantoms for virtual imaging trials. However, generating anatomically plausible reconstructions of organ surfaces from computed tomography scans remains challenging f
Externí odkaz:
http://arxiv.org/abs/2309.08289
Autor:
Tushar, Fakrul Islam, Dahal, Lavsen, Sotoudeh-Paima, Saman, Abadi, Ehsan, Segars, W. Paul, Samei, Ehsan, Lo, Joseph Y.
The credibility of AI models in medical imaging is often challenged by reproducibility issues and obscured clinical insights, a reality highlighted during the COVID-19 pandemic by many reports of near-perfect artificial intelligence (AI) models that
Externí odkaz:
http://arxiv.org/abs/2308.09730
Autor:
Tushar, Fakrul Islam, Abadi, Ehsan, Sotoudeh-Paima, Saman, Fricks, Rafael B., Mazurowski, Maciej A., Segars, W. Paul, Samei, Ehsan, Lo, Joseph Y.
Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerous applications of deep learning for COVID-19 diagnosis. Virtua
Externí odkaz:
http://arxiv.org/abs/2203.03074
Autor:
Tushar, Fakrul Islam, Nujaim, Husam, Fu, Wanyi, Abadi, Ehsan, Mazurowski, Maciej A., Samei, Ehsan, Segars, William P., Lo, Joseph Y.
Organ segmentation of medical images is a key step in virtual imaging trials. However, organ segmentation datasets are limited in terms of quality (because labels cover only a few organs) and quantity (since case numbers are limited). In this study,
Externí odkaz:
http://arxiv.org/abs/2203.01934
Autor:
Tushar, Fakrul Islam, D'Anniballe, Vincent M., Rubin, Geoffrey D., Samei, Ehsan, Lo, Joseph Y.
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD). For CT specifically, interpreting the performance of CAD algorithm
Externí odkaz:
http://arxiv.org/abs/2202.11709