Search Results - "Felder, Federico"

From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis by de la Orden Kett Morais, Sean R, Felder, Federico N, Walsh, Simon L F

“…The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable…”
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Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges by Felder, Federico N, Walsh, Simon L F

“…The advent of quantitative computed tomography (QCT) and artificial intelligence (AI) using high-resolution computed tomography data has revolutionised the way…”
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Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge by Nan, Yang, Xing, Xiaodan, Wang, Shiyi, Tang, Zeyu, Felder, Federico N, Zhang, Sheng, Ledda, Roberta Eufrasia, Ding, Xiaoliu, Yu, Ruiqi, Liu, Weiping, Shi, Feng, Sun, Tianyang, Cao, Zehong, Zhang, Minghui, Gu, Yun, Zhang, Hanxiao, Gao, Jian, Wang, Pingyu, Tang, Wen, Yu, Pengxin, Kang, Han, Chen, Junqiang, Lu, Xing, Zhang, Boyu, Mamalakis, Michail, Prinzi, Francesco, Carlini, Gianluca, Cuneo, Lisa, Banerjee, Abhirup, Xing, Zhaohu, Zhu, Lei, Mesbah, Zacharia, Jain, Dhruv, Mayet, Tsiry, Yuan, Hongyu, Lyu, Qing, Qayyum, Abdul, Mazher, Moona, Wells, Athol, Walsh, Simon LF, Yang, Guang

“…•This paper investigates the capacity of AI models for airway modelling on national datasets with paired clinical metadata.•We evaluated AI models against…”
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Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method by Wang, Shiyi, Nan, Yang, Zhang, Sheng, Felder, Federico, Xing, Xiaodan, Fang, Yingying, Del Ser, Javier, Walsh, Simon L.F., Yang, Guang

“…In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors…”
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The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus? by Xing, Xiaodan, Nan, Yang, Felder, Federico, Walsh, Simon, Yang, Guang

“…Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images…”
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Machine learning in radiology: the new frontier in interstitial lung diseases by Barnes, Hayley, Humphries, Stephen M, George, Peter M, Assayag, Deborah, Glaspole, Ian, Mackintosh, John A, Corte, Tamera J, Glassberg, Marilyn, Johannson, Kerri A, Calandriello, Lucio, Felder, Federico, Wells, Athol, Walsh, Simon

“…Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication…”
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Description of a single centre cohort of patients with systemic sclerosis from the University Hospital of Buenos Aires and factors associated with lung function deterioration. A retrospective study by Enghelmayer, Juan I., López Meiller, María José, Vallejos, Ailín, Felder, Federico, Pertuz, María Milena, Arias, Tamara, Legarreta, Cora G., Acuña, Silvana, Leiva, Sebastián, Barrios, Vanesa, Dubinsky, Diana

“…Given the paucity of data in Latin America and especially in Argentina regarding the epidemiology of SSc, the prevalence of ILD, its course, and particularly…”
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Description of a single centre cohort of patients with systemic sclerosis from the University Hospital of Buenos Aires and factors associated with lung function deterioration. A retrospective study by Enghelmayer, Juan I., López Meiller, María José, Vallejos, Ailín, Felder, Federico, Pertuz, María Milena, Arias, Tamara, Legarreta, Cora G., Acuña, Silvana, Leiva, Sebastián, Barrios, Vanesa, Dubinsky, Diana

“…Given the paucity of data in Latin America and especially in Argentina regarding the epidemiology of SSc, the prevalence of ILD, its course, and particularly…”
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The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus? by Xing, Xiaodan, Nan, Yang, Felder, Federico, Walsh, Simon, Yang, Guang

“…Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images…”
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Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants by Wang, Shiyi, Nan, Yang, N, Felder Federico, Zhang, Sheng, F, Walsh Simon L, Yang, Guang

“…Each medical segmentation task should be considered with a specific AI algorithm based on its scenario so that the most accurate prediction model can be…”
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You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images by Xing, Xiaodan, Felder, Federico, Nan, Yang, Papanastasiou, Giorgos, Simon, Walsh, Yang, Guang

“…Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. The selection of synthesis models…”
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Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method by Wang, Shiyi, Nan, Yang, Zhang, Sheng, Felder, Federico, Xing, Xiaodan, Fang, Yingying, Del Ser, Javier, Walsh, Simon L F, Yang, Guang

“…In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation. Additionally, Deep Learning (DL) methods face…”
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Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge by Nan, Yang, Xing, Xiaodan, Wang, Shiyi, Tang, Zeyu, Felder, Federico N, Zhang, Sheng, Ledda, Roberta Eufrasia, Ding, Xiaoliu, Yu, Ruiqi, Liu, Weiping, Shi, Feng, Sun, Tianyang, Cao, Zehong, Zhang, Minghui, Gu, Yun, Zhang, Hanxiao, Gao, Jian, Wang, Pingyu, Tang, Wen, Yu, Pengxin, Kang, Han, Chen, Junqiang, Lu, Xing, Zhang, Boyu, Mamalakis, Michail, Prinzi, Francesco, Carlini, Gianluca, Cuneo, Lisa, Banerjee, Abhirup, Xing, Zhaohu, Zhu, Lei, Mesbah, Zacharia, Jain, Dhruv, Mayet, Tsiry, Yuan, Hongyu, Lyu, Qing, Qayyum, Abdul, Mazher, Moona, Wells, Athol, Walsh, Simon LF, Yang, Guang

“…Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of…”
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