Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality. Note that this paradigm is still at its infancy and limited by closely related open issues: 1) how to learn robust representations in an unsupervised manner from unlabelled biomedical microscopy images of low diversity in samples? and 2) how to obtain the most significant representations demanded by a high-quality segmentation? Aiming at these issues, this study proposes a knowledge-based learning framework (TOWER) towards enhanced recognition of biomedical microscopy images, which works in three phases by synergizing contrastive learning and generative learning methods: 1) Sample Space Diversification: Reconstructive proxy tasks have been enabled to embed a priori knowledge with context highlighted to diversify the expanded sample space; 2) Enhanced Representation Learning: Informative noise-contrastive estimation loss regularizes the encoder to enhance representation learning of annotation-free images; 3) Correlated Optimization: Optimization operations in pre-training the encoder and the decoder have been correlated via image restoration from proxy tasks, targeting the need for semantic segmentation. Experiments have been conducted on public datasets of biomedical microscopy images against the state-of-the-art counterparts (e.g., SimCLR and BYOL), and results demonstrate that: TOWER statistically excels in all self-supervised methods, achieving a Dice improvement of 1.38 percentage points over SimCLR. TOWER also has potential in multi-modality medical image analysis and enables label-efficient semi-supervised learning, e.g., reducing the annotation cost by up to 99% in pathological classification.
翻译:训练前自监督的自我监督已经成为建立可靠的神经网络的先验选择,以自动识别大规模生物医学显微镜,这些显微镜通常没有注解,没有语义,没有质量保障。请注意,这种模式仍处于初创阶段,受到密切相关的开放问题的限制:(1) 如何以未经贴标签的生物医学显微镜在样本中具有低多样性的未经监视的方式从未贴标签的生物医学显微镜中学习强健的表述?和(2) 如何获得高质量分解所要求的最显著的表述?针对这些问题,本研究报告建议建立一个基于知识的学习框架(TOWER),以强化对生物医学显微镜的识别,这些图像通常都是无注解的。 相关医学化框架(TOWOWER)通过同步对比对比对比对比对比性学习和基因化学习方法在三个阶段运作。 试样空间扩大后,重新构造代理任务得以嵌入先验知识;(2) 强化演示: 感动性噪动性噪动估计损失将精度降低感官对说明性改进的代谢方法的学习;(3) 与超值的医学成像机分析:SyLILOD