Search Results for author: Tanvir Mahmud

Found 7 papers, 1 papers with code

AVE-CLIP: AudioCLIP-based Multi-window Temporal Transformer for Audio Visual Event Localization

no code implementations11 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.

audio-visual event localization

CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans

no code implementations3 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.

Lesion Segmentation severity prediction

A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network

no code implementations3 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.

Human Activity Recognition Time Series

Automatic Diagnosis of Malaria from Thin Blood Smear Images using Deep Convolutional Neural Network with Multi-Resolution Feature Fusion

no code implementations9 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.

CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans

no code implementations2 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.

Lesion Segmentation

ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images

no code implementations17 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.

BIG-bench Machine Learning Computed Tomography (CT)

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