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Cell detection and cell type classification from biomedical images play animportant role for high-throughput imaging and various clinical application.While classification of single cell sample can be performed with standardcomputer vision and machine learning methods, analysis of multi-label samples(region containing congregating cells) is more challenging, as separation ofindividual cells can be difficult (e.g. touching cells) or even impossible(e.g. overlapping cells). As multi-instance images are common in analyzing RedBlood Cell (RBC) for Sickle Cell Disease (SCD) diagnosis, we develop andimplement a multi-instance cell detection and classification framework toaddress this challenge. The framework firstly trains a region proposal modelbased on Region-based Convolutional Network (RCNN) to obtain bounding-boxes ofregions potentially containing single or multiple cells from input microscopicimages, which are extracted as image patches. High-level image features arethen calculated from image patches through a pre-trained Convolutional NeuralNetwork (CNN) with ResNet-50 structure. Using these image features inputs, sixnetworks are then trained to make multi-label prediction of whether a givenpatch contains cells belonging to a specific cell type. As the six networks aretrained with image patches consisting of both individual cells andtouching/overlapping cells, they can effectively recognize cell types that arepresented in multi-instance image samples. Finally, for the purpose of SCDtesting, we train another machine learning classifier to predict whether thegiven image patch contains abnormal cell type based on outputs from the sixnetworks. Testing result of the proposed framework shows that it can achievegood performance in automatic cell detection and classification.

Multi-label Detection and Classification of Red Blood Cells in Microscopic Images