Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images.

The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1832 WSIs from 479 BCCs, divided into training and validation (1435 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to (1) detect the presence of tumor (two classes), (2) classify the tumor into low and high-risk subtypes (three classes), and (3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency.

Development of forecast models for COVID-19 hospital admissions using anonymized and aggregated mobile network data.

Reliable forecast of COVID-19 hospital admissions in near-term horizons can help enable effective resource management which is vital in reducing pressure from healthcare services. The use of mobile network data has come to attention in response to COVID-19 pandemic leveraged on their ability in capturing people social behavior. Crucially, we show that there are latent features in irreversibly anonymized and aggregated mobile network data that carry useful information in relation to the spread of SARS-CoV-2 virus. We describe development of the forecast models using such features for prediction of COVID-19 hospital admissions in near-term horizons (21 days). In a case study, we verified the approach for two hospitals in Sweden, Sahlgrenska University Hospital and Södra Älvsborgs Hospital, working closely with the experts engaged in the hospital resource planning. Importantly, the results of the forecast models were used in year 2021 by logisticians at the hospitals as one of the main inputs for their decisions regarding resource management.

Making every SAR point count: the development of Chemistry Connect for the large-scale integration of structure and bioactivity data.

The increase in drug research output from patent applications, together with the expansion of public data collections, such as ChEMBL and PubChem BioAssay, has made it essential for pharmaceutical companies to integrate both internal and external 'SAR estate'. The AstraZeneca response has been the development of an enterprise application, Chemistry Connect, containing 45 million unique chemical structures from 18 internal and external data sources. It includes merged compound-to-assay-to-result-to-target relationships extracted from patents, papers and internal data. Users can explore connections between these by searching using drug names or synonyms, chemical structures, patent numbers and target protein identifiers at a scale not previously available.