Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Hashmi, Anees Ur Rehman"'
Autor:
Qazi, Mohammad Areeb, Hashmi, Anees Ur Rehman, Sanjeev, Santosh, Almakky, Ibrahim, Saeed, Numan, Gonzalez, Camila, 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
Brain tumor segmentation is a fundamental step in assessing a patient's cancer progression. However, manual segmentation demands significant expert time to identify tumors in 3D multimodal brain MRI scans accurately. This reliance on manual segmentat
Externí odkaz:
http://arxiv.org/abs/2405.02852
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
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging. However, the lack of detailed investigations into the performance of exp
Externí odkaz:
http://arxiv.org/abs/2403.18996
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:
Maani, Fadillah, Hashmi, Anees Ur Rehman, Aljuboory, Mariam, Saeed, Numan, Sobirov, Ikboljon, Yaqub, Mohammad
Automated segmentation proves to be a valuable tool in precisely detecting tumors within medical images. The accurate identification and segmentation of tumor types hold paramount importance in diagnosing, monitoring, and treating highly fatal brain
Externí odkaz:
http://arxiv.org/abs/2403.09262
Autor:
Hashmi, Anees Ur Rehman, Almakky, Ibrahim, Qazi, Mohammad Areeb, Sanjeev, Santosh, Papineni, Vijay Ram, Jagdish, Jagalpathy, Yaqub, Mohammad
Large-scale generative models have demonstrated impressive capabilities in producing visually compelling images, with increasing applications in medical imaging. However, they continue to grapple with hallucination challenges and the generation of an
Externí odkaz:
http://arxiv.org/abs/2403.09240
Autor:
Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel
The 12-volume set LNCS 15001 - 15012 constitutes the proceedings of the 27th International Conferenc on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, which took place in Marrakesh, Morocco, during October 6–10, 2024. MI
This two-volume set LNCS 14859-14860 constitutes the proceedings of the 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024, held in Manchester, UK, during July 24–26, 2024. The 59 full papers included in this book were c