# Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data

arXiv: https://arxiv.org/abs/2203.04289 · HTML: https://mlsanity.com/paper/2203.04289/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Unsupervised Lesion Detection via Image Restoration with a Normative Prior](https://mlsanity.com/paper/2005.00031/) (2020)
- [Automatic Shortcut Removal for Self-Supervised Representation Learning](https://mlsanity.com/paper/2002.08822/) (2020)
- [Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models](https://mlsanity.com/paper/1907.00058/) (2019)
- [Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images](https://mlsanity.com/paper/1704.00406/) (2017)
- [Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images](https://mlsanity.com/paper/1907.01376/) (2019)

## Built on by (most cited first, 3 of 3)

- [Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration](https://mlsanity.com/paper/2301.04525/) (2023)
- [Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder](https://mlsanity.com/paper/2205.05194/) (2022)
- [Self-supervised pseudo-colorizing of masked cells](https://mlsanity.com/paper/2302.05968/) (2023)

## Abstract (arXiv)

In this work, we propose two novel methodologies to study temporal and morphological phenotypic effects caused by different experimental conditions using imaging data. As a proof of concept, we apply them to analyze drug effects in 2D cancer cell cultures. We train a convolutional autoencoder on 1M images dataset with random augmentations and multi-crops to use as feature extractor. We systematically compare it to the pretrained state-of-the-art models. We further use the feature extractor in two ways. First, we apply distance-based analysis and dynamic time warping to cluster temporal patterns of 31 drugs. We identify clusters allowing annotation of drugs as having cytotoxic, cytostatic, mixed or no effect. Second, we implement an adversarial/regularized learning setup to improve classification of 31 drugs and visualize image regions that contribute to the improvement. We increase top-3 classification accuracy by 8% on average and mine examples of morphological feature importance maps. We provide the feature extractor and the weights to foster transfer learning applications in biology. We also discuss utility of other pretrained models and applicability of our methods to other types of biomedical data.