Výsledky vyhledávání - "Borhani, Amir"

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A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation

Autor: Biswas, Koushik, Pal, Ridal, Patel, Shaswat, Jha, Debesh, Karri, Meghana, Reza, Amit, Durak, Gorkem, Medetalibeyoglu, Alpay, Antalek, Matthew, Velichko, Yury, Ladner, Daniela, Borhani, Amir, Bagci, Ulas

Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans an

Externí odkaz: http://arxiv.org/abs/2408.05692

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MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation

Autor: Jha, Debesh, Tomar, Nikhil Kumar, Biswas, Koushik, Durak, Gorkem, Antalek, Matthew, Zhang, Zheyuan, Wang, Bin, Rahman, Md Mostafijur, Pan, Hongyi, Medetalibeyoglu, Alpay, Velichko, Yury, Ladner, Daniela, Borhani, Amir, Bagci, Ulas

Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges of heterogeneity in organ shapes, sizes, and complex anatomical relatio

Externí odkaz: http://arxiv.org/abs/2405.06166

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PAM-UNet: Shifting Attention on Region of Interest in Medical Images

Autor: Das, Abhijit, Jha, Debesh, Gorade, Vandan, Biswas, Koushik, Pan, Hongyi, Zhang, Zheyuan, Ladner, Daniela P., Velichko, Yury, Borhani, Amir, Bagci, Ulas

Computer-aided segmentation methods can assist medical personnel in improving diagnostic outcomes. While recent advancements like UNet and its variants have shown promise, they face a critical challenge: balancing accuracy with computational efficien

Externí odkaz: http://arxiv.org/abs/2405.01503

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Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques

Autor: Hong, Ziliang, Jha, Debesh, Biswas, Koushik, Zhang, Zheyuan, Velichko, Yury, Yazici, Cemal, Tirkes, Temel, Borhani, Amir, Turkbey, Baris, Medetalibeyoglu, Alpay, Durak, Gorkem, Bagci, Ulas

Identifying peri-pancreatic edema is a pivotal indicator for identifying disease progression and prognosis, emphasizing the critical need for accurate detection and assessment in pancreatitis diagnosis and management. This study \textit{introduces a

Externí odkaz: http://arxiv.org/abs/2404.17064

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CT Liver Segmentation via PVT-based Encoding and Refined Decoding

Autor: Jha, Debesh, Tomar, Nikhil Kumar, Biswas, Koushik, Durak, Gorkem, Medetalibeyoglu, Alpay, Antalek, Matthew, Velichko, Yury, Ladner, Daniela, Borhani, Amir, Bagci, Ulas

Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning. Computer-aided diagnosis systems promise to improve the precision of liver disease diagnosis, disease progression, and treatment planning. In respo

Externí odkaz: http://arxiv.org/abs/2401.09630

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Transformer based Generative Adversarial Network for Liver Segmentation

Autor: Demir, Ugur, Zhang, Zheyuan, Wang, Bin, Antalek, Matthew, Keles, Elif, Jha, Debesh, Borhani, Amir, Ladner, Daniela, Bagci, Ulas

Publikováno v: ICPAI 2021

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the s

Externí odkaz: http://arxiv.org/abs/2205.10663

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