Systems and methods of correcting batch effect in biological images
A system for the rectification of batch-induced distortions and the extraction of phenotypic attributes from biological images is presented. This system encompasses a deep-learning generative adversarial network model, expertly tailored to converse with and modify the contrast of input images, resulting in the creation of output images. A discriminator, operating as a feedback mechanism, discriminates the output image against the input image, ensuring precise image contrast conversion. Integral to this system is a morphology distillator, having a dual classifier framework comprising a batch classifier and a cell type/state classifier. This distillator adeptly identifies phenotypic characteristics and batch-related disparities, subsequently eradicating these batch variations from the output image. The resultant augmentation enriches the cellular information within the image.
1 . A system for correcting batch effects by removing systematic variations in biological images from equipment, reagents, sample preparation, or imaging conditions and extracting phenotypic features in biological images, comprising:
a deep-learning generative adversarial network model trained to determine an underlying structure of an input image and remove image artifacts, wherein the deep-learning generative adversarial network model comprises an autoencoder architecture for:
extracting morphological features of the input image;
reconstructing an output image; and
pretraining components of the deep-learning generative adversarial network model for image reconstruction;
a discriminator that classifies the output image by comparing it with the input image for identifying and rectifying residual batch-related variations, serving as a part of a concurrent refinement cycle to achieve accurate image reconstruction and contrast conversion; and
a morphology distillator comprising a batch classifier and a cell type/state classifier for recognizing phenotypic features and batch variations and removing the batch variations in the output image through a feedback mechanism in the concurrent refinement cycle, wherein the batch classifier employs domain adaptation to mitigate the influence of batch effects while retaining biologically significant cellular information identified by the cell type/state classifier;
wherein the deep-learning generative adversarial network model is retrained by the concurrent refinement cycle with parameters of the batch classifier and the cell-type/state classifier being frozen during batch effect correction; and
wherein the frozen parameters of the batch classifier and the cell-type/state classifier are used to update parameters of the autoencoder architecture and the discriminator, thereby guiding the batch effect correction while preserving the biologically significant cellular information and ensuring image prediction accuracy.
2 . The system of claim 1 , wherein the batch classifier and the cell type/state classifier are pretrained by the autoencoder to identify batch and cell information.
3 . The system of claim 1 , wherein the deep-learning generative adversarial network model can be optionally pretrained with an image contrast translation functionality, so as to perform an image contrast conversion for providing an augmented output image.
4 . The system of claim 1 , wherein the morphology distillator is integrated into a bottleneck region and the output layer of the deep-learning generative adversarial network model, establishing a feedback loop to separate the phenotypic features and batch variations.
5 . The system of claim 1 , wherein the cell type/state classifier within the morphology distillator employs a self-supervised learning mechanism to capture and distinguish cellular phenotypes to provide automated cellular classification.
6 . The system of claim 1 , wherein the batch distillation potency of the deep-learning generative adversarial network model can be elevated by a process of segmenting the batch classifier within the morphology distillator at a bottleneck stage into multiple miniature classifiers, and/or a process of periodic retraining at a predetermined interval.
7 . The system of claim 1 , wherein the discriminator employs a convolutional neural network to assess and guide the contrast adjustment process for translation of image contrast.
8 . The system of claim 1 , wherein the morphology distillator incorporates a self-attention mechanism to selectively focus on informative regions within the input image, to extract phenotypic features from the input image.
9 . The system of claim 1 , wherein the training to determine an underlying structure of an input image and remove image artifacts includes training on diverse and heterogeneous biological datasets to learn different imaging modalities, cell types, and experimental conditions.
10 . The system of claim 1 , wherein the system is implemented in a cloud-based platform for remote access to batch correction and phenotypic feature extraction.