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Fig. 2 | ROBOMECH Journal

Fig. 2

From: High accuracy detection for T-cells and B-cells using deep convolutional neural networks

Fig. 2

Flow chart of training AlexCAN. Firstly, a CNN < Cell/Background > is re-trained using the original dataset with the labels of cell and background. Then, training images dyed with a single dye are exhaustively searched, and the trained CNN < Cell/Background > is used to create a new dataset with labels T-cell and B-cell. After that, another CNN < T-cell/B-cell > is trained from the newly created dataset, which can distinguish T-cells from B-cells. Then, cells in the original dataset are annotated by using the newly trained CNN < T-cell/B-cell >. We obtain the extended dataset by merging the newly created dataset and annotated original dataset. Lastly, AlexCAN is re-trained from the extended dataset as our final classifier, that can separate cells from background and can also annotate them as T-cell and B-cell

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