1. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks
- Author
-
Oliver Dürr and Beate Sick
- Subjects
0301 basic medicine ,Support Vector Machine ,Computer science ,006: Spezielle Computerverfahren ,Bioinformatics ,Biochemistry ,Convolutional neural network ,Analytical Chemistry ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Image Processing, Computer-Assisted ,Humans ,Standard test image ,business.industry ,Deep learning ,Cognitive neuroscience of visual object recognition ,Cell-based assays ,High-content screening ,Pattern recognition ,Linear discriminant analysis ,Computer-assisted image processing ,Random forest ,Support vector machine ,030104 developmental biology ,Single-cell classification ,Neural networks (computer) ,Molecular Medicine ,Artificial intelligence ,Neural Networks, Computer ,Single-Cell Analysis ,business ,Classifier (UML) ,030217 neurology & neurosurgery ,Algorithms ,Software ,Biotechnology - Abstract
Published online: 12 February 2016 Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.
- Published
- 2015