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Your search keyword '"Bajwa, Usama Ijaz"' showing total 147 results
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147 results on '"Bajwa, Usama Ijaz"'

5. Resource-efficient domain adaptive pre-training for medical images

Author

Mehmood, Yasar, Bajwa, Usama Ijaz, and Sun, Xianfang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing
Abstract

The deep learning-based analysis of medical images suffers from data scarcity because of high annotation costs and privacy concerns. Researchers in this domain have used transfer learning to avoid overfitting when using complex architectures. However, the domain differences between pre-training and downstream data hamper the performance of the downstream task. Some recent studies have successfully used domain-adaptive pre-training (DAPT) to address this issue. In DAPT, models are initialized with the generic dataset pre-trained weights, and further pre-training is performed using a moderately sized in-domain dataset (medical images). Although this technique achieved good results for the downstream tasks in terms of accuracy and robustness, it is computationally expensive even when the datasets for DAPT are moderately sized. These compute-intensive techniques and models impact the environment negatively and create an uneven playing field for researchers with limited resources. This study proposed computationally efficient DAPT without compromising the downstream accuracy and robustness. This study proposes three techniques for this purpose, where the first (partial DAPT) performs DAPT on a subset of layers. The second one adopts a hybrid strategy (hybrid DAPT) by performing partial DAPT for a few epochs and then full DAPT for the remaining epochs. The third technique performs DAPT on simplified variants of the base architecture. The results showed that compared to the standard DAPT (full DAPT), the hybrid DAPT technique achieved better performance on the development and external datasets. In contrast, simplified architectures (after DAPT) achieved the best robustness while achieving modest performance on the development dataset .

Published
2022

8. Deception Detection in Videos using the Facial Action Coding System

Author

Ahmed, Hammad Ud Din, Bajwa, Usama Ijaz, Zhang, Fan, and Anwar, Muhammad Waqas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Facts are important in decision making in every situation, which is why it is important to catch deceptive information before they are accepted as facts. Deception detection in videos has gained traction in recent times for its various real-life application. In our approach, we extract facial action units using the facial action coding system which we use as parameters for training a deep learning model. We specifically use long short-term memory (LSTM) which we trained using the real-life trial dataset and it provided one of the best facial only approaches to deception detection. We also tested cross-dataset validation using the Real-life trial dataset, the Silesian Deception Dataset, and the Bag-of-lies Deception Dataset which has not yet been attempted by anyone else for a deception detection system. We tested and compared all datasets amongst each other individually and collectively using the same deep learning training model. The results show that adding different datasets for training worsen the accuracy of the model. One of the primary reasons is that the nature of these datasets vastly differs from one another.

Published
2021

15. FERNet: A Convolutional Neural Networks Based Robust Model to Recognize Human Facial Expressions

Author

Gilanie, Ghulam, Rehman, Nasira, Bajwa, Usama Ijaz, Sharif, Sabiha, Ullah, Hafeez, Mushtaq, Muhammad Faheem, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ghazali, Rozaida, editor, Mohd Nawi, Nazri, editor, Deris, Mustafa Mat, editor, Abawajy, Jemal H., editor, and Arbaiy, Nureize, editor

Published
2022
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27. Brain tumor grade classification using multi‐step pre‐training.

Author

Mehmood, Yasar, Bajwa, Usama Ijaz, and Anwar, Muhammad Waqas

Subjects
*DEEP learning, *TUMOR classification, *BRAIN tumors, *TUMOR grading, *COMPUTER-assisted image analysis (Medicine), *DIAGNOSTIC imaging
Abstract

Medical images offer a non‐invasive method to diagnose different diseases, but using them manually produces unreliable results. Modern deep learning architectures and techniques are computed and data‐intensive, making them difficult to use for relatively smaller datasets of medical images. Transfer learning has been used as a remedy for the problem mentioned above. However, the domain difference between the datasets used for pre‐training (e.g., ImageNet) and the target datasets, like medical images, negatively impacts the transfer learning results. Recently, many researchers have used additional pre‐training called domain‐adaptive pre‐training (DAPT) using the data from the target domain (e.g., medical images) before using the model on the target tasks to achieve superior performance. This study proposes a variant of DAPT by performing it on a subset of the architecture. It has achieved state‐of‐the‐art performance for brain tumor grading on the BraTS 2019 while being computationally efficient. [ABSTRACT FROM AUTHOR]

Published
2024
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