Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

Data processing and learning has become a spearhead for the advancement of medicine,
with pathology and laboratory medicine has no exception. The incorporation of scientific …

We address the challenging problem of whole slide image (WSI) classification. WSIs have
very high resolutions and usually lack localized annotations. WSI classification can be cast …

Abstract The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital
histology. Deep learning (DL) models have been trained on TCGA to predict numerous …

Traditional image-based survival prediction models rely on discriminative patch labeling
which make those methods not scalable to extend to large datasets. Recent studies have …

Recently, artificial intelligence and machine learning in general have demonstrated
remarkable performances in many tasks, from image processing to natural language …

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …

Once users have shared their data online, it is generally difficult for them to revoke access
and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because …

The development of decision support systems for pathology and their deployment in clinical
practice have been hindered by the need for large manually annotated datasets. To …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …