End-to-End Learning via a Convolutional Neural Network for Cancer Cell Line Classification
This work addresses the need for automated and accurate classification of cancer cell types in medical imaging, though it is incremental as it applies an existing deep learning method to a specific domain.
The authors tackled the problem of classifying breast cancer cell lines from brightfield microscopy images by developing a 6-layer convolutional neural network that operates end-to-end without prior feature extraction, achieving a 99% accuracy on a dataset of 1,241 images.
Computer Vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various Machine Learning and Machine Vision algorithms. We developed a Convolutional Neural Network model that classifies MDA-MB-468 and MCF7 breast cancer cells via brightfield microscopy images without the need of any prior feature extraction. Our 6-layer Convolutional Neural Network is directly trained, validated and tested on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell line in an end-to-end fashion, allowing a system to distinguish between different cancer cell types. The model takes in as input imaged breast cancer cell line and then outputs the cell line type (MDA-MB-468 or MCF7) as predicted probabilities between the two classes. Our model scored a 99% Accuracy.