粘土
发表于 2025-3-30 10:37:34
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Introvert
发表于 2025-3-30 13:50:20
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无法治愈
发表于 2025-3-30 18:42:47
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音乐学者
发表于 2025-3-30 22:31:22
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenglity and reduce labeling time. These systems, however, are generally highly dependent on their training domain and show poor applicability to unseen domains. In histopathology, these domain shifts can result from various sources, including different slide scanning systems used to digitize histologic
Expand
发表于 2025-3-31 01:02:26
Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopatts manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven artificial intelligence (AI) methods have been developed to automatically detect mitotic figures. However, these methods often gen
arousal
发表于 2025-3-31 06:00:19
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maladorit
发表于 2025-3-31 09:56:09
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喧闹
发表于 2025-3-31 16:39:04
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注射器
发表于 2025-3-31 19:17:30
Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challengewith tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners for this task. We present a short summary of the approach employed by the . team to address this challenge. Our approach is based on a hybrid
肉体
发表于 2025-4-1 01:39:01
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