  Cell detection and cell type classification from biomedical images play an
important role for high-throughput imaging and various clinical application.
While classification of single cell sample can be performed with standard
computer vision and machine learning methods, analysis of multi-label samples
(region containing congregating cells) is more challenging, as separation of
individual cells can be difficult (e.g. touching cells) or even impossible
(e.g. overlapping cells). As multi-instance images are common in analyzing Red
Blood Cell (RBC) for Sickle Cell Disease (SCD) diagnosis, we develop and
implement a multi-instance cell detection and classification framework to
address this challenge. The framework firstly trains a region proposal model
based on Region-based Convolutional Network (RCNN) to obtain bounding-boxes of
regions potentially containing single or multiple cells from input microscopic
images, which are extracted as image patches. High-level image features are
then calculated from image patches through a pre-trained Convolutional Neural
Network (CNN) with ResNet-50 structure. Using these image features inputs, six
networks are then trained to make multi-label prediction of whether a given
patch contains cells belonging to a specific cell type. As the six networks are
trained with image patches consisting of both individual cells and
touching/overlapping cells, they can effectively recognize cell types that are
presented in multi-instance image samples. Finally, for the purpose of SCD
testing, we train another machine learning classifier to predict whether the
given image patch contains abnormal cell type based on outputs from the six
networks. Testing result of the proposed framework shows that it can achieve
good performance in automatic cell detection and classification.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning