no code implementations • 24 Mar 2024 • Tanvir Mahmud, Burhaneddin Yaman, Chun-Hao Liu, Diana Marculescu
Using this insight, we introduce PaPr, a method for substantially pruning redundant patches with minimal accuracy loss using lightweight ConvNets across a variety of deep learning architectures, including ViTs, ConvNets, and hybrid transformers, without any re-training.
no code implementations • 14 May 2023 • Burhaneddin Yaman, Tanvir Mahmud, Chun-Hao Liu
We propose an embarrassingly simple method -- instance-aware repeat factor sampling (IRFS) to address the problem of imbalanced data in long-tailed object detection.
no code implementations • 7 Mar 2023 • Md Awsafur Rahman, Bishmoy Paul, Tanvir Mahmud, Shaikh Anowarul Fattah
In this paper, based on contextual image feature fusion (CIFF), a deep neural network (CIFF-Net) is proposed, which integrates patient-level contextual information into the traditional approaches for improved Melanoma diagnosis by concurrent multi-image comparative method.
no code implementations • 11 Oct 2022 • Tanvir Mahmud, Diana Marculescu
An audio-visual event (AVE) is denoted by the correspondence of the visual and auditory signals in a video segment.
no code implementations • 3 Jan 2021 • Tanvir Mahmud, A. Q. M. Sazzad Sayyed, Shaikh Anowarul Fattah, Sun-Yuan Kung
In this paper, we have proposed a novel multi-stage training approach that increases diversity in this feature extraction process to make accurate recognition of actions by combining varieties of features extracted from diverse perspectives.
no code implementations • 3 Jan 2021 • Tanvir Mahmud, Md. Jahin Alam, Sakib Chowdhury, Shams Nafisa Ali, Md Maisoon Rahman, Shaikh Anowarul Fattah, Mohammad Saquib
A multi-phase optimization strategy is introduced for solving the challenges of complicated diagnosis at a very early stage of infection, where an efficient lesion segmentation network is optimized initially which is later integrated into a joint optimization framework for the diagnosis and severity prediction tasks providing feature enhancement of the infected regions.
no code implementations • 9 Dec 2020 • Tanvir Mahmud, Shaikh Anowarul Fattah
In this paper, an end-to-end deep learning-based approach is proposed for faster diagnosis of malaria from thin blood smear images by making efficient optimizations of features extracted from diversified receptive fields.
no code implementations • 2 Dec 2020 • Tanvir Mahmud, Md Awsafur Rahman, Shaikh Anowarul Fattah, Sun-Yuan Kung
Moreover, a multi-scale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation.
no code implementations • 17 Nov 2020 • Ankan Ghosh Dastider, Mohseu Rashid Subah, Farhan Sadik, Tanvir Mahmud, Shaikh Anowarul Fattah
Automatic disease detection using machine learning-based techniques from X-ray and computed tomography (CT) can play a major role in the frontline to assist medical professionals during the current outbreak of COVID-19.
1 code implementation • Computers in Biology and Medicine 2020 • Tanvir Mahmud, Md Awsafur Rahman, Shaikh Anowarul Fattah
Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients.