Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found well-replicated genes such as HERC2 and OCA2 for hair and eye color, and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.

Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018 . We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

14 15 *Jointly supervised this project 16 17 Neural networks have been seldomly leveraged in population genomics due to the 18 computational burden and challenge of interpretability. Here, we propose GenNet, a novel 19 open-source deep learning framework for predicting phenotype from genotype. In this 20 framework, public prior biological knowledge is used to construct interpretable and 21 memory-efficient neural network architectures. These architectures obtain good predictive 22 performance for multiple traits and complex diseases, opening the door for neural 23 networks in population genomics. 24 25phenotypes at more and more independent loci. To illustrate, the latest GWAS for body height 30 based on 700,000 individuals identified more than 3000 near-independent significantly 31 associated single nucleotide polymorphisms (SNPs) 1 . This information, used in combination with 32 annotated biological databases such as: NCBI RefSeq, KEGG, Reactome and GTEx has proven 33 to be highly valuable for understanding the underlying biological mechanisms of complex 34 diseases 2-6 . In this paper, we propose a new framework, GenNet, that integrates these biological 35 data sources for discovery and interpretability in an end-to-end deep learning framework for 36 predicting phenotypes. 37Deep learning is the state of the art in many domains such as medical image analysis and natural 38 language processing because of its flexibility and modeling capabilities 7,8 . In many cases, deep 39 learning yields better performance compared to traditional approaches, since it can model highly 40 non-linear relations and scales very well with data size. However, this often comes at the cost of 41 interpretability, since there is a trade-off between complexity and interpretability 9,10 . 42 Additionally, when it comes to genotype data, the number of learnable parameters increases 43 dramatically because of the large input size, making it infeasible to use classical neural networks 44 in this domain. To overcome previous limitations, we propose a new framework, GenNet, in 45 which different types of biological information are used to define biologically plausible neural 46 network architectures, avoiding this trade-off and creating interpretable neural networks for 47 129 polygenic risk scoring for schizophrenia, which have AUC values on the order of 0.70 (ranging 130 0.49-0.85) 15 . This is noteworthy since in this study the schizophrenia predictions are based on 131 whole exome sequencing data as opposed to GWAS arrays spanning the whole genome.132 133 134 Discussion 135Here, we present a novel framework to train interpretable neural networks for phenotype 136 prediction from genotype. The proposed neural networks have connections defined by prior 137 biological knowledge only, reducing the number of connections and therefore the number of