This paper, by Tang et al. introduces a self-supervised shifted window transformer for medical image analysis on a large computed tomography data set that is capable of extracting features at various resolutions from the whole image. #SMILEJournalClub

This paper, by Athey and Wager explores an extension of random forests called causal forests and its' ability to understand how treatment effects vary across different samples. #SMILEJournalClub

This paper, by Groos et al. uses a deep learning method to predict cerebral palsy in infants from 9 to 18 weeks' corrected age. Their deep learning approach outperforms the clinically recommended general movement assessment (GMA) tool. #SMILEJournalClub

The authors introduce a joint polygenic risk score (PRS) and multimodal brain imaging study which classifies between schizophrenic (SZ) and healthy controls (HC). Their novel contribution is associating PRS-SZ with multimodal brain patterns to distinguish SZ and HC.

...and multi-severity grading. This system can potentially serve as a cost-effective screening procedure that can significantly reduce the work load for ophthalmologists.

This week's #SMILEJournalClub, "The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions" by Littlejohns et al. revisits 100,000 participants with plans to expand the dataset with additional imaging modalities.

This paper, "Meta-matching as a simple framework to translate phenotypic predictive models from big to small data", by He et al. exploits phenotypic correlations to improve prediction performance of new phenotypes in small-scale studies. #SMILEJournalClub

This week's journal club paper by Schulz et al. compares the sample size scaling of linear/non-linear machine learning and deep learning using the UK Biobank dataset and the MNIST and Fashion-MNIST benchmark datasets. #SMILEJournalClub

An interesting paper by Abrol et al. emphasizes the importance of representation learning for deep learning in comparison to feature-selected standard machine learning approaches through multiple classification and regression tasks. #SMILEJournalClub

The scope of this dataset includes various imaging modalities, health records, genetics, biological phenotyping, etc. The collection of this data has the potential to provide early insight into disease mechanisms and predictions.

The authors model a deep artificial neural network to the ventral and dorsal pathways of the visual cortex. They employ a self-supervised predictive loss function and show that self-supervised learning has the potential to explain the functional properties of the visual cortex.

In response, their contribution addressed the limitations across these studies. They published an open-source software called clinica. This software has multiple modular procedures for processing data from widely used datasets.