15 Nov 2018

NeurIPS 2018

Authors: Craig A. Glastonbury (BenevolentAI), Michael Ferlaino, Christoffer Nellåker and Cecilia M. Lindgren (Big Data Institute, University of Oxford)


Biological imaging data are often partially confounded or contain unwanted variability. Examples of such phenomena include variable lighting across microscopy image captures, stain intensity variation in histological slides, and batch effects for high throughput drug screening assays. Therefore, to develop "fair" models which generalise well to unseen examples, it is crucial to learn data representations that are insensitive to nuisance factors of variation. In this paper, we present a strategy based on adversarial training, capable of learning unsupervised representations invariant to confounders. As an empirical validation of our method, we use deep convolutional autoencoders to learn unbiased cellular representations from microscopy imaging.

Back to publications

Latest publications

24 Aug 2023
Associating biological context with protein-protein interactions through text mining at PubMed scale
Read more
07 Dec 2022
NeurIPS 2022
sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion
Read more
07 Dec 2022
EMNLP 2022
Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval
Read more