Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Mohammadi, Sadegh"'
Mammography is crucial for breast cancer surveillance and early diagnosis. However, analyzing mammography images is a demanding task for radiologists, who often review hundreds of mammograms daily, leading to overdiagnosis and overtreatment. Computer
Externí odkaz:
http://arxiv.org/abs/2407.14326
Autor:
Jimenez-Perez, Guillermo, Osorio, Pedro, Cersovsky, Josef, Montalt-Tordera, Javier, Hooge, Jens, Vogler, Steffen, Mohammadi, Sadegh
Diffusion models (DMs) have emerged as powerful foundation models for a variety of tasks, with a large focus in synthetic image generation. However, their requirement of large annotated datasets for training limits their applicability in medical imag
Externí odkaz:
http://arxiv.org/abs/2407.11594
Autor:
Osorio, Pedro, Jimenez-Perez, Guillermo, Montalt-Tordera, Javier, Hooge, Jens, Duran-Ballester, Guillem, Singh, Shivam, Radbruch, Moritz, Bach, Ute, Schroeder, Sabrina, Siudak, Krystyna, Vienenkoetter, Julia, Lawrenz, Bettina, Mohammadi, Sadegh
Artificial Intelligence (AI) based image analysis has an immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful s
Externí odkaz:
http://arxiv.org/abs/2312.09792
The classification of gigapixel histopathology images with deep multiple instance learning models has become a critical task in digital pathology and precision medicine. In this work, we propose a Transformer-based multiple instance learning approach
Externí odkaz:
http://arxiv.org/abs/2308.12634
Audio-based classification techniques on body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of cough as the main biomarker, other body sounds also have the potential to detec
Externí odkaz:
http://arxiv.org/abs/2204.10581
Publikováno v:
Proceedings of Machine Learning for Health, PMLR 158:54-74, 2021
Transfer learning has become a standard practice to mitigate the lack of labeled data in medical classification tasks. Whereas finetuning a downstream task using supervised ImageNet pretrained features is straightforward and extensively investigated
Externí odkaz:
http://arxiv.org/abs/2108.10048
Publikováno v:
International Journal of Ethics and Society, Vol 4, Iss 3, Pp 10-15 (2022)
Background: Diverse and wide-ranging capacities have led to a special dynamic in bilateral relations between Iran and China in recent years. So that in different areas, these bilateral relations have been constantly expanding and deepening. From this
Externí odkaz:
https://doaj.org/article/39acba1014144fcbb8e27ea5b8c4bdbf
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Publikováno v:
Sensors (14248220); Oct2024, Vol. 24 Issue 19, p6176, 21p
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