Zobrazeno 1 - 10
of 13 667
pro vyhledávání: '"Ulas, A"'
Autor:
Tur, Yalcin, Cicek, Vedat, Cinar, Tufan, Keles, Elif, Allen, Bradlay D., Savas, Hatice, Durak, Gorkem, Medetalibeyoglu, Alpay, Bagci, Ulas
Pulmonary Embolism (PE) is a serious cardiovascular condition that remains a leading cause of mortality and critical illness, underscoring the need for enhanced diagnostic strategies. Conventional clinical methods have limited success in predicting 3
Externí odkaz:
http://arxiv.org/abs/2411.18063
Autor:
Hendrix, Rutger, Salanitri, Federica Proietto, Spampinato, Concetto, Palazzo, Simone, Bagci, Ulas
We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts
Externí odkaz:
http://arxiv.org/abs/2411.10071
Autor:
Pan, Hongyi, Hong, Ziliang, Durak, Gorkem, Keles, Elif, Aktas, Halil Ertugrul, Taktak, Yavuz, Medetalibeyoglu, Alpay, Zhang, Zheyuan, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Miller, Frank, Keswani, Rajesh N., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification ut
Externí odkaz:
http://arxiv.org/abs/2411.05697
Autor:
Bengtsson, Max, Keles, Elif, Durak, Gorkem, Anwar, Syed, Velichko, Yuri S., Linguraru, Marius G., Waanders, Angela J., Bagci, Ulas
In this paper, we present a novel approach for segmenting pediatric brain tumors using a deep learning architecture, inspired by expert radiologists' segmentation strategies. Our model delineates four distinct tumor labels and is benchmarked on a hel
Externí odkaz:
http://arxiv.org/abs/2411.01390
Autor:
Pan, Hongyi, Durak, Gorkem, Zhang, Zheyuan, Taktak, Yavuz, Keles, Elif, Aktas, Halil Ertugrul, Medetalibeyoglu, Alpay, Velichko, Yury, Spampinato, Concetto, Schoots, Ivo, Bruno, Marco J., Keswani, Rajesh N., Tiwari, Pallavi, Bolan, Candice, Gonda, Tamas, Goggins, Michael G., Wallace, Michael B., Xu, Ziyue, Bagci, Ulas
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face d
Externí odkaz:
http://arxiv.org/abs/2410.22530
Autor:
Afonja, Tejumade, Sheth, Ivaxi, Binkyte, Ruta, Hanif, Waqar, Ulas, Thomas, Becker, Matthias, Fritz, Mario
Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understanding these networks is crucial for uncovering disease mechanisms and iden
Externí odkaz:
http://arxiv.org/abs/2410.15828
Autor:
Wang, Bin, Choudhuri, Anwesa, Zheng, Meng, Gao, Zhongpai, Planche, Benjamin, Deng, Andong, Liu, Qin, Chen, Terrence, Bagci, Ulas, Wu, Ziyan
Interactive segmentation aims to accurately segment target objects with minimal user interactions. However, current methods often fail to accurately separate target objects from the background, due to a limited understanding of order, the relative de
Externí odkaz:
http://arxiv.org/abs/2410.12214
Autor:
Jha, Debesh, Susladkar, Onkar Kishor, Gorade, Vandan, Keles, Elif, Antalek, Matthew, Seyithanoglu, Deniz, Cebeci, Timurhan, Aktas, Halil Ertugrul, Kartal, Gulbiz Dagoglu, Kaymakoglu, Sabahattin, Erturk, Sukru Mehmet, Velichko, Yuri, Ladner, Daniela, Borhani, Amir A., Medetalibeyoglu, Alpay, Durak, Gorkem, Bagci, Ulas
Liver cirrhosis, the end stage of chronic liver disease, is characterized by extensive bridging fibrosis and nodular regeneration, leading to an increased risk of liver failure, complications of portal hypertension, malignancy and death. Early diagno
Externí odkaz:
http://arxiv.org/abs/2410.16296
Federated Learning (FL) offers a powerful strategy for training machine learning models across decentralized datasets while maintaining data privacy, yet domain shifts among clients can degrade performance, particularly in medical imaging tasks like
Externí odkaz:
http://arxiv.org/abs/2410.02044
Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question generation, the creation of di
Externí odkaz:
http://arxiv.org/abs/2409.18263