Metadata-guided Consistency Learning for High Content Images

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Fältnamn	Indikatorer	Metadata
000		02871naa a2200337 4500
001		oai:DiVA.org:kth-346566
003		SwePub
008		240517s2023 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3465662 URI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a kon2 swepub-publicationtype
100	1	a Fredin Haslum, Johanu KTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab4 aut0 (Swepub:kth)u13aplea
245	1 0	a Metadata-guided Consistency Learning for High Content Images
264	1	c 2023
338		a print2 rdacarrier
500		a QC 20240521
520		a 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.
650	7	a NATURVETENSKAPx Data- och informationsvetenskap0 (SwePub)1022 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciences0 (SwePub)1022 hsv//eng
653		a Datalogi
653		a Computer Science
700	1	a Matsoukas, Christosu KTH,Beräkningsvetenskap och beräkningsteknik (CST)4 aut0 (Swepub:kth)u1ft226d
700	1	a Leuchowius, Karl-Johan4 aut
700	1	a Müllers, Erik4 aut
700	1	a Smith, Kevin,d 1975-u KTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab4 aut0 (Swepub:kth)u1l33jpf
710	2	a KTHb Beräkningsvetenskap och beräkningsteknik (CST)4 org
773	0	t PLMR: Volume 227: Medical Imaging with Deep Learning, 10-12 July 2023, Nashville, TN, USA
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-346566

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Av författaren/redakt...: Fredin Haslum, J ...; Matsoukas, Chris ...; Leuchowius, Karl ...; Müllers, Erik; Smith, Kevin, 19 ...

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