Metadata-guided Consistency Learning for High Content Images

Fredin Haslum, JohanKTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab,AstraZeneca, Gothenburg, Sweden (author)

QC 20240711
QC 20240521
High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods have shown great success on natural images, and offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which are caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a self-supervised approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, leading to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mechanism of action.

Matsoukas, ChristosKTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab,AstraZeneca, Gothenburg, Sweden(Swepub:kth)u1ft226d (author)
Leuchowius, Karl JohanAstraZeneca, Gothenburg, Sweden (author)
Müllers, ErikAstraZeneca, Gothenburg, Sweden (author)
Smith, Kevin,1975-KTH,Science for Life Laboratory, SciLifeLab,Beräkningsvetenskap och beräkningsteknik (CST)(Swepub:kth)u1l33jpf (author)
KTHBeräkningsvetenskap och beräkningsteknik (CST) (creator_code:org_t)

In:Medical Imaging with Deep Learning 2023, MIDL 2023: ML Research Press, s. 918-936
In:PLMR: Volume 227: Medical Imaging with Deep Learning, 10-12 July 2023, Nashville, TN, USA: ML Research Press, s. 918-936

By the author/editor: Fredin Haslum, J ...; Matsoukas, Chris ...; Leuchowius, Karl ...; Müllers, Erik; Smith, Kevin, 19 ...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

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