GitHub / EhtashamBillah / Acute-Lymphoblastic-Leukemia-cell-classification-using-Bayesian-Convolutional-Neural-Networks

In this project, we deploy the Bayesian Convolution Neural Networks (BCNN), proposed by Gal and Ghahramani [2015] to classify microscopic images of blood samples (lymphocyte cells). The data contains 260 microscopic images of cancerous and non-cancerous lymphocyte cells. We experiment with different network structures to obtain the model that return lowest error rate in classifying the images. We estimate the uncertainty for the predictions made by the models which in turn can assist a doctor in better decision making. The Stochastic Regularization Technique (SRT), popularly known as Dropout is utilized in the BCNN structure to obtain the Bayesian interpretation.

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Topics: bayesian, bayesian-inference, cancer-imaging-research, convolutional-neural-networks, variational-inference

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