有效的半监督语义细分电子显微镜癌症图像的稀疏注释
Lucas Pagano1,2, Guillaume Thibault1, Walid Bousselham1
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
深度学习模型通过自动化细胞核和细胞核的细分来显著加速用于癌症研究的电子显微镜图像的分析. 这种方法克服了耗时的手动分析瓶.
科学领域:
- 在瘤学瘤学.
- 生物医学成像技术 生物医学成像技术
- 计算生物学 计算生物学
背景情况:
- 电子显微镜 (EM) 提供纳米分辨率成像,对于了解癌症治疗耐药性至关重要.
- 手动分析EM数据是一个重要的瓶,每个样本需要几个月的时间.
- 深度学习 (DL) 为自动化和加速EM图像分析提供了一个有前途的解决方案.
结论:
- 更复杂的DL模型在细分性能方面提供了相对的收益.
- 半监督学习有效地利用未标记的数据来增强细分.
- 这些进展对于缓解癌症研究中手动细分瓶至关重要.
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