  Accurately and quickly binuclear cell (BC) detection plays a significant role
in predicting the risk of leukemia and other malignant tumors. However, manual
microscopy counting is time-consuming and lacks objectivity. Moreover, with the
limitation of staining quality and diversity of morphology features in BC
microscopy whole slide images (WSIs), traditional image processing approaches
are helpless. To overcome this challenge, we propose a two-stage detection
method inspired by the structure prior of BC based on deep learning, which
cascades to implement BCs coarse detection at the WSI-level and fine-grained
classification in patch-level. The coarse detection network is a multi-task
detection framework based on circular bounding boxes for cells detection, and
central key points for nucleus detection. The circle representation reduces the
degrees of freedom, mitigates the effect of surrounding impurities compared to
usual rectangular boxes and can be rotation invariant in WSI. Detecting key
points in the nucleus can assist network perception and be used for
unsupervised color layer segmentation in later fine-grained classification. The
fine classification network consists of a background region suppression module
based on color layer mask supervision and a key region selection module based
on a transformer due to its global modeling capability. Additionally, an
unsupervised and unpaired cytoplasm generator network is firstly proposed to
expand the long-tailed distribution dataset. Finally, experiments are performed
on BC multicenter datasets. The proposed BC fine detection method outperforms
other benchmarks in almost all the evaluation criteria, providing clarification
and support for tasks such as cancer screenings.
