  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.
