representation-learning-papers-in-single-cell-microscopy

This repository holds an updated track of representation learning papers with application in single cell microscopy data.

review

• Self-Supervised Representation Learning: Introduction, Advances and Challenges [paper]

• Image-based cell phenotyping with deep learning [paper]

self-supervised

• Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting [paper][code]

• Self-supervised dense representation learning for live-cell microscopy with time arrow prediction [paper] [code]

• Self-supervised deep learning encodes high-resolution features of protein subcellular localization [paper] [code]

• Deep Representation Learning for Image-Based Cell Profiling [paper]

• Unbiased single-cell morphology with self-supervised vision transformers [paper] [code]

• Microsnoop: A generalist tool for microscopy image representation [paper] [code]

• Self-Supervised Representation Learning for High-Content Screening [paper]

weakly-supervised

• Weakly Supervised Learning of Single-Cell Feature Embeddings [paper] [code]

• Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology [paper] [code]

• Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels [paper] [code]