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pro vyhledávání: '"Imran, Abdullah Al"'
Autor:
Ward, Tyler, Imran, Abdullah-Al-Zubaer
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation,
Externí odkaz:
http://arxiv.org/abs/2412.08575
Autor:
Imran, Abdullah Al, Ishmam, Md Farhan
In resource constraint settings, adaptation to downstream classification tasks involves fine-tuning the final layer of a classifier (i.e. classification head) while keeping rest of the model weights frozen. Multi-Layer Perceptron (MLP) heads fine-tun
Externí odkaz:
http://arxiv.org/abs/2408.08803
Autor:
Imran, Abdullah-Al-Zubaer, Wang, Sen, Pal, Debashish, Dutta, Sandeep, Patel, Bhavik, Zucker, Evan, Wang, Adam
Purpose: Estimation of patient-specific organ doses is required for more comprehensive dose metrics, such as effective dose. Currently, available methods are performed retrospectively using the CT images themselves, which can only be done after the s
Externí odkaz:
http://arxiv.org/abs/2312.15354
This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features cat
Externí odkaz:
http://arxiv.org/abs/2310.11465
Medical image classification is one of the most important tasks for computer-aided diagnosis. Deep learning models, particularly convolutional neural networks, have been successfully used for disease classification from medical images, facilitated by
Externí odkaz:
http://arxiv.org/abs/2305.02927
Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-supervised learning and self-supervised learning offer effectiveness through
Externí odkaz:
http://arxiv.org/abs/2304.05047
Autor:
Lee, Wonkyeong, Wagner, Fabian, Galdran, Adrian, Shi, Yongyi, Xia, Wenjun, Wang, Ge, Mou, Xuanqin, Ahamed, Md. Atik, Imran, Abdullah Al Zubaer, Oh, Ji Eun, Kim, Kyungsang, Baek, Jong Tak, Lee, Dongheon, Hong, Boohwi, Tempelman, Philip, Lyu, Donghang, Kuiper, Adrian, van Blokland, Lars, Calisto, Maria Baldeon, Hsieh, Scott, Han, Minah, Baek, Jongduk, Maier, Andreas, Wang, Adam, Gold, Garry Evan, Choi, Jang-Hwan
Publikováno v:
In Medical Image Analysis January 2025 99
Autor:
Sarker, Yeahia, Imran, Abdullah-Al-Zubaer, Ahamed, Md Hafiz, Chakrabortty, Ripon K., Ryan, Michael J., Das, Sajal K.
Deep learning-based super-resolution methods have shown great promise, especially for single image super-resolution (SISR) tasks. Despite the performance gain, these methods are limited due to their reliance on copious data for model training. In add
Externí odkaz:
http://arxiv.org/abs/2204.01711
Deep learning-based models, when trained in a fully-supervised manner, can be effective in performing complex image analysis tasks, although contingent upon the availability of large labeled datasets. Especially in the medical imaging domain, however
Externí odkaz:
http://arxiv.org/abs/2110.13185
CT image quality is heavily reliant on radiation dose, which causes a trade-off between radiation dose and image quality that affects the subsequent image-based diagnostic performance. However, high radiation can be harmful to both patients and opera
Externí odkaz:
http://arxiv.org/abs/2105.07153