Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Razavian, Narges"'
Autor:
Liu, Kangning, Zhu, Weicheng, Shen, Yiqiu, Liu, Sheng, Razavian, Narges, Geras, Krzysztof J., Fernandez-Granda, Carlos
Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), wh
Externí odkaz:
http://arxiv.org/abs/2210.09452
Publikováno v:
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:1504-1522, 2022
Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis rate. Factors influencing recurrence and metastasis are currently unknown and there are no distinct histopathological or morphological features indicating the risks of recurrenc
Externí odkaz:
http://arxiv.org/abs/2203.12204
Autor:
Liu, Sheng, Kaku, Aakash, Zhu, Weicheng, Leibovich, Matan, Mohan, Sreyas, Yu, Boyang, Huang, Haoxiang, Zanna, Laure, Razavian, Narges, Niles-Weed, Jonathan, Fernandez-Granda, Carlos
Publikováno v:
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13746-13781, 2022
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a
Externí odkaz:
http://arxiv.org/abs/2111.10734
Domain adaptation and covariate shift are big issues in deep learning and they ultimately affect any causal inference algorithms that rely on deep neural networks. Causal effect variational autoencoder (CEVAE) is trained to predict the outcome given
Externí odkaz:
http://arxiv.org/abs/2111.08656
We show that bringing intermediate layers' representations of two augmented versions of an image closer together in self-supervised learning helps to improve the momentum contrastive (MoCo) method. To this end, in addition to the contrastive loss, we
Externí odkaz:
http://arxiv.org/abs/2110.14805
Autor:
Shamout, Farah E., Shen, Yiqiu, Wu, Nan, Kaku, Aakash, Park, Jungkyu, Makino, Taro, Jastrzębski, Stanisław, Witowski, Jan, Wang, Duo, Zhang, Ben, Dogra, Siddhant, Cao, Meng, Razavian, Narges, Kudlowitz, David, Azour, Lea, Moore, William, Lui, Yvonne W., Aphinyanaphongs, Yindalon, Fernandez-Granda, Carlos, Geras, Krzysztof J.
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a
Externí odkaz:
http://arxiv.org/abs/2008.01774
We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed to first fit the training data with clean labels during an "early lea
Externí odkaz:
http://arxiv.org/abs/2007.00151
Clinical interactions are initially recorded and documented in free text medical notes. ICD coding is the task of classifying and coding all diagnoses, symptoms and procedures associated with a patient's visit. The process is often manual and extreme
Externí odkaz:
http://arxiv.org/abs/2006.03685
Autor:
Zhu, Weicheng, Razavian, Narges
Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of diseases and lab-disease correlations can be informative when only a subset
Externí odkaz:
http://arxiv.org/abs/1912.03761
Autor:
Aguet, François, Akiyama, Yo, Anand, Shankara, Anurag, Meenakshi, Babur, Özgün, Bavarva, Jasmin, Birger, Chet, Birrer, Michael J., Cantley, Lewis C., Cao, Song, Carr, Steven A., Ceccarelli, Michele, Chan, Daniel W., Chinnaiyan, Arul M., Cho, Hanbyul, Chowdhury, Shrabanti, Cieslik, Marcin P., Clauser, Karl R., Colaprico, Antonio, Zhou, Daniel Cui, da Veiga Leprevost, Felipe, Day, Corbin, Dhanasekaran, Saravana M., Domagalski, Marcin J., Dou, Yongchao, Druker, Brian J., Edwards, Nathan, Ellis, Matthew J., Selvan, Myvizhi Esai, Foltz, Steven M., Francis, Alicia, Geffen, Yifat, Getz, Gad, Gonzalez Robles, Tania J., Gosline, Sara J.C., Gümüş, Zeynep H., Heiman, David I., Hiltke, Tara, Hostetter, Galen, Hu, Yingwei, Huang, Chen, Huntsman, Emily, Iavarone, Antonio, Jaehnig, Eric J., Jewell, Scott D., Ji, Jiayi, Jiang, Wen, Johnson, Jared L., Katsnelson, Lizabeth, Ketchum, Karen A., Kolodziejczak, Iga, Krug, Karsten, Kumar-Sinha, Chandan, Lei, Jonathan T., Liang, Wen-Wei, Liao, Yuxing, Lindgren, Caleb M., Liu, Tao, Ma, Weiping, Rodrigues, Fernanda Martins, McKerrow, Wilson, Mesri, Mehdi, Nesvizhskii, Alexey I., Newton, Chelsea J., Oldroyd, Robert, Paulovich, Amanda G., Payne, Samuel H., Petralia, Francesca, Pugliese, Pietro, Reva, Boris, Rykunov, Dmitry, Satpathy, Shankha, Savage, Sara R., Schadt, Eric E., Schnaubelt, Michael, Schürer, Stephan, Shi, Zhiao, Smith, Richard D., Song, Xiaoyu, Song, Yizhe, Stathias, Vasileios, Storrs, Erik P., Terekhanova, Nadezhda V., Thangudu, Ratna R., Thiagarajan, Mathangi, Tignor, Nicole, Wang, Liang-Bo, Wang, Pei, Wang, Ying, Wen, Bo, Wiznerowicz, Maciej, Wu, Yige, Wyczalkowski, Matthew A., Yao, Lijun, Yaron, Tomer M., Yi, Xinpei, Zhang, Bing, Zhang, Hui, Zhang, Qing, Zhang, Xu, Zhang, Zhen, Wang, Joshua M., Hong, Runyu, Demicco, Elizabeth G., Tan, Jimin, Lazcano, Rossana, Moreira, Andre L., Li, Yize, Calinawan, Anna, Razavian, Narges, Schraink, Tobias, Gillette, Michael A., Omenn, Gilbert S., An, Eunkyung, Rodriguez, Henry, Tsirigos, Aristotelis, Ruggles, Kelly V., Ding, Li, Robles, Ana I., Mani, D.R., Rodland, Karin D., Lazar, Alexander J., Liu, Wenke, Fenyö, David
Publikováno v:
In Cell Reports Medicine 19 September 2023 4(9)
