摘要
Recent advancements in deep learning and computational power have opened new possibilities that were once unattainable. Researchers are now eager to transfer their expertise in deep learning to various domains, including medical diagnostics, autonomous systems, and remote sensing. In the medical field, the application of deep learning promises to reduce the workload of healthcare professionals, streamline screening processes, and improve time efficiency. Breast cancer remains a significant challenge for many women, with mass-type cancers having increased difficulty due to their heterogeneity and anomalies. Addressing this issue requires a more detailed investigation. In this study, we propose an innovative training strategy aimed at improving the precision and F1 score of breast cancer detection models. Furthermore, we introduce a novel method, named the Batch Scheduler, which dynamically adjusts batch sizes during the training phase, rather than maintaining a constant size throughout. This approach has been shown to improve the performance of the existing system by 0.4%. For our training and testing, we used 'ConvNext' equipped with pre-trained weights, which further contributed to the robustness of our model.
原文 | ???core.languages.en_GB??? |
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主出版物標題 | AVSS 2024 - 20th IEEE International Conference on Advanced Video and Signal-Based Surveillance |
發行者 | Institute of Electrical and Electronics Engineers Inc. |
版本 | 2024 |
ISBN(電子) | 9798350374285 |
DOIs | |
出版狀態 | 已出版 - 2024 |
事件 | 20th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2024 - Niagara Falls, Canada 持續時間: 15 7月 2024 → 16 7月 2024 |
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???event.eventtypes.event.conference??? | 20th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2024 |
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國家/地區 | Canada |
城市 | Niagara Falls |
期間 | 15/07/24 → 16/07/24 |