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Traditional supervised 3D medical image segmentation models need voxel-level annotations, which require huge human effort, time, and cost. Semi-supervised learning (SSL) addresses this limitation of supervised learning by facilitating learning with a
Externí odkaz:
http://arxiv.org/abs/2407.05088
Autor:
Kumari, Suruchi, Singh, Pravendra
Semi-supervised medical image segmentation has gained growing interest due to its ability to utilize unannotated data. The current state-of-the-art methods mostly rely on pseudo-labeling within a co-training framework. These methods depend on a singl
Externí odkaz:
http://arxiv.org/abs/2405.07256
Python, as a multi-paradigm language known for its ease of integration with other languages, has gained significant attention among verification engineers recently. A Python-based verification environment capitalizes on open-source frameworks such as
Externí odkaz:
http://arxiv.org/abs/2407.10317
Being the most widely used language across the world due to its simplicity and with 35 keywords (v3.7), Python attracts both hardware and software engineers. Python-based verification environment leverages open-source libraries such as cocotb and coc
Externí odkaz:
http://arxiv.org/abs/2407.10312
Autor:
Kumari, Suruchi, Singh, Pravendra
The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to
Externí odkaz:
http://arxiv.org/abs/2310.06557
Autor:
Kumari, Suruchi, Singh, Pravendra
Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortu
Externí odkaz:
http://arxiv.org/abs/2308.01265
Medical image classification is a challenging task due to the scarcity of labeled samples and class imbalance caused by the high variance in disease prevalence. Semi-supervised learning (SSL) methods can mitigate these challenges by leveraging both l
Externí odkaz:
http://arxiv.org/abs/2307.04610
Autor:
Kumari, Suruchi, Singh, Pravendra
Publikováno v:
In Computers in Biology and Medicine March 2024 170
Publikováno v:
In Biomedical Signal Processing and Control March 2024 89
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