no code implementations • 1 Dec 2023 • Asifullah Khan, Zunaira Rauf, Abdul Rehman Khan, Saima Rathore, Saddam Hussain Khan, Najmus Saher Shah, Umair Farooq, Hifsa Asif, Aqsa Asif, Umme Zahoora, Rafi Ullah Khalil, Suleman Qamar, Umme Hani Asif, Faiza Babar Khan, Abdul Majid, Jeonghwan Gwak
This survey paper provides a detailed review of the recent advancements in ViTs and HVTs for medical image segmentation.
1 code implementation • 30 Jul 2023 • Debesh Jha, Vanshali Sharma, Debapriya Banik, Debayan Bhattacharya, Kaushiki Roy, Steven A. Hicks, Nikhil Kumar Tomar, Vajira Thambawita, Adrian Krenzer, Ge-Peng Ji, Sahadev Poudel, George Batchkala, Saruar Alam, Awadelrahman M. A. Ahmed, Quoc-Huy Trinh, Zeshan Khan, Tien-Phat Nguyen, Shruti Shrestha, Sabari Nathan, Jeonghwan Gwak, Ritika K. Jha, Zheyuan Zhang, Alexander Schlaefer, Debotosh Bhattacharjee, M. K. Bhuyan, Pradip K. Das, Deng-Ping Fan, Sravanthi Parsa, Sharib Ali, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Ulas Bagci
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps.
no code implementations • 16 May 2023 • Momina Liaqat Ali, Zunaira Rauf, Asifullah Khan, Anabia Sohail, Rafi Ullah, Jeonghwan Gwak
To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images.
no code implementations • 11 Apr 2023 • Muhammad Umar Farooq, Zahid Ullah, Jeonghwan Gwak
To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism.
no code implementations • journal 2021 • Shikha Dubey, Abhijeet Boragule, Jeonghwan Gwak, Moongu Jeon
We propose a framework, Deep-network with Multiple Ranking Measures(DMRMs), which addresses context-dependency using a joint learning technique for motion and appearance features.
Ranked #10 on Anomaly Detection In Surveillance Videos on UCF-Crime