See all upcoming seminars in LäsIT and seminar web pages at the homepage for the PhD studentseminars, TDB, Vi2, Theory and Applications Seminars (TAS) @ UpMARC., Department of Mathematics and The Stockholm Logic Seminar.

Disputation | PhD defense
Karl Bengtsson Bernander: Equivariant Neural Networks for Biomedical Image Analysis
Location: ÅNG 101121, Sonja Lyttkens, Time: 13:15

Opponent: professor Michal Kozubek from Masaryk University
Supervisor: professor Ingela Nyström
While artificial intelligence and deep learning have revolutionized many fields in the last decade, one of the key drivers has been access to data. This is especially true in biomedical image analysis where expert annotated data is hard to come by. The combination of Convolutional Neural Networks (CNNs) with data augmentation has proven successful in increasing the amount of training data at the cost of overfitting. In this thesis, equivariant neural networks have been used to extend the equivariant properties of CNNs to more transformations than translations. The networks have been trained and evaluated on biomedical image datasets, including bright-field microscopy images of cytological samples indicating oral cancer, and transmission electron microscopy images of virus samples. By designing the networks to be equivariant to e.g. rotations, it is shown that the need for data augmentation is reduced, that less overfitting occurs, and that convergence during training is faster. Furthermore, equivariant neural networks are more data efficient than CNNs, as demonstrated by scaling laws. These benefits are not present in all problem settings and which benefits will occur is somewhat unpredictable. We have identified that the results to some extent depend on architectures, hyperparameters and datasets. Further research may broaden the performed studies to explain how the results occur with new theory.

Thesis in DIVA

Disputation | PhD defence
8 March
Camille Clouard: A computational and statistical framework for cost-effective genotyping combining pooling and imputation
Location: ÅNG 101195, Time: 10:15

Opponent: Prof. Christine Baes, Department of Animal Biosciences, University of Guelph, Canada
Supervisor: Carl Nettelblad


The information conveyed by genetic markers, such as single nucleotide polymorphisms (SNPs), has been widely used in biomedical research to study human diseases and is increasingly valued in agriculture for genomic selection purposes. Specific markers can be identified as a genetic signature that correlates with certain characteristics in a living organism, e.g. a susceptibility to disease or high-yield traits. Capturing these signatures with sufficient statistical power often requires large volumes of data, with thousands of samples to be analysed and potentially millions of genetic markers to be screened. Relevant effects are particularly delicate to detect when the genetic variations involved occur at low frequencies.

The cost of producing such marker genotype data is therefore a critical part of the analysis. Despite recent technological advances, production costs can still be prohibitive on a large scale and genotype imputation strategies have been developed to address this issue. Genotype imputation methods have been extensively studied in human data and, to a lesser extent, in crop and animal species. A recognised weakness of imputation methods is their lower accuracy in predicting the genotypes for rare variants, whereas those can be highly informative in association studies and improve the accuracy of genomic selection. In this respect, pooling strategies can be well suited to complement imputation, as pooling is efficient at capturing the low-frequency items in a population. Pooling also reduces the number of genotyping tests required, making its use in combination with imputation a cost-effective compromise between accurate but expensive high-density genotyping of each sample individually and stand-alone imputation. However, due to the nature of genotype data and the limitations of genotype testing techniques, decoding pooled genotypes into unique data resolutions is challenging.

In this work, we study the characteristics of decoded genotype data from pooled observations with a specific pooling scheme using the examples of a human cohort and a population of inbred wheat lines. We propose different inference strategies to reconstruct the genotypes before devising them as input to imputation, and we reflect on how the reconstructed distributions affect the results of imputation methods such as tree-based haplotype clustering or coalescent models.

Thesis in DIVA

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