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pro vyhledávání: '"Qazi, Mohammad Areeb"'
Autor:
Qazi, Mohammad Areeb, Hashmi, Anees Ur Rehman, Sanjeev, Santosh, Almakky, Ibrahim, Saeed, Numan, Yaqub, Mohammad
Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application. Issues like catastrophic forgetting and distribution shifts in the continuously evolving data stream increase the g
Externí odkaz:
http://arxiv.org/abs/2405.13482
Autor:
Qazi, Mohammad Areeb, Almakky, Ibrahim, Hashmi, Anees Ur Rehman, Sanjeev, Santosh, Yaqub, Mohammad
Continual learning, the ability to acquire knowledge from new data while retaining previously learned information, is a fundamental challenge in machine learning. Various approaches, including memory replay, knowledge distillation, model regularizati
Externí odkaz:
http://arxiv.org/abs/2404.14099
Autor:
Sanjeev, Santosh, Zhaksylyk, Nuren, Almakky, Ibrahim, Hashmi, Anees Ur Rehman, Qazi, Mohammad Areeb, Yaqub, Mohammad
The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP. Model soups averages multiple fine-tuned models aiming to improve performance on In-Domain (ID)
Externí odkaz:
http://arxiv.org/abs/2403.13341
Autor:
Almakky, Ibrahim, Sanjeev, Santosh, Hashmi, Anees Ur Rehman, Qazi, Mohammad Areeb, Yaqub, Mohammad
Transfer learning has become a powerful tool to initialize deep learning models to achieve faster convergence and higher performance. This is especially useful in the medical imaging analysis domain, where data scarcity limits possible performance ga
Externí odkaz:
http://arxiv.org/abs/2403.11646
Autor:
Hashmi, Anees Ur Rehman, Almakky, Ibrahim, Qazi, Mohammad Areeb, Sanjeev, Santosh, Papineni, Vijay Ram, Mahapatra, Dwarikanath, Yaqub, Mohammad
Large-scale generative models have demonstrated impressive capacity in producing visually compelling images, with increasing applications in medical imaging. However, they continue to grapple with the challenge of image hallucination and the generati
Externí odkaz:
http://arxiv.org/abs/2403.09240
Autor:
Qazi, Mohammad Areeb, Alam, Mohammed Talha, Almakky, Ibrahim, Diehl, Werner Gerhard, Bricker, Leanne, Yaqub, Mohammad
Precise estimation of fetal biometry parameters from ultrasound images is vital for evaluating fetal growth, monitoring health, and identifying potential complications reliably. However, the automated computerized segmentation of the fetal head, abdo
Externí odkaz:
http://arxiv.org/abs/2311.09607
Publikováno v:
IEEE Access, Vol 10, Pp 8502-8517 (2022)
Hearing-impaired people use sign language to express their thoughts and emotions and reinforce information delivered in daily conversations. Though they make a significant percentage of any population, the majority of people can’t interact with the
Externí odkaz:
https://doaj.org/article/3b1dcd78df65482facfec34c25935e41
Publikováno v:
IEEE Access, Vol 9, Pp 155949-155976 (2021)
The interconnected digital world is generating enormous data that must be secured from unauthorized access. Advancement in technologies and new innovative methods applied by attackers play an instrumental role in breaching data security. Public key C
Externí odkaz:
https://doaj.org/article/a580ff96bad743eb86f2d94a89777f5c
Publikováno v:
IEEE Access, Vol 9, Pp 155949-155976 (2021)
The interconnected digital world is generating enormous data that must be secured from unauthorized access. Advancement in technologies and new innovative methods applied by attackers play an instrumental role in breaching data security. Public key C
Autor:
Qazi Mohammad Areeb, Mohammad Nadeem
Publikováno v:
Advances in Data Computing, Communication and Security ISBN: 9789811684029
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50559869a98993865fe06c1242a5f647
https://doi.org/10.1007/978-981-16-8403-6_21
https://doi.org/10.1007/978-981-16-8403-6_21