Health & Life Sciences

Data Science Discovery Program

The Data Science Discovery Program incubates and accelerates data science and AI projects with academic institutions, government agencies, non-profit, and industry partners across the world. The Data Science Discovery Program is a partnership between BIDS, DSUS, and D-Lab.

In 2015, the program was launched at Berkeley Institute for Data Science. It started...

Computational Precision Health

The joint UC Berkeley-UCSF Program in Computational Precision Health (CPH) will bridge medicine, statistics, and computation to improve the quality, efficiency, and equity of medicine and population health. BIDS Faculty Affiliate Maya Petersen is a CPH Co-Director, and CPH Core and Affiliate Faculty include BIDS Faculty Affiliates ...

BIDS releases Mothra 1.0

August 30, 2022

BIDS team members Stéfan J. van der Walt and Alex de Siqueira have released Mothra 1.0, the first major version of the software that was published in cooperation with researchers from London’s Natural History Museum and the University of Southampton last April. Mothra analyzes images of lepidopterans — mainly butterflies and moths — using deep learning and image processing.

Medical Imaging Research Using Deep Learning

Drs. Maryam Vareth and Akram Bayat offer this project (#1) through UC Berkeley's Undergraduate Research Apprentice Program (URAP).

One of the fastest growing fields of research in medical imaging during the last several years is the use of machine learning methods for image reconstruction.

This project aims to use deep learning approaches in image reconstruction to accelerate Magnetic Resonance Imaging (MRI) acquisition and in result reduce MRI examination times for patients. Two of the most influential development in this area during the...

Open-source software for generating synthetic electronic health records

BIDS Health and Life Sciences Lead Maryam Vareth offers this project (#2) through UC Berkeley's Undergraduate Research Apprentice Program (URAP).

Research access to electronic health record (EHR) data is limited due to patient privacy concerns. Creating synthetic EHR data (data that models realistic patterns and yet does not correspond to real patient records) provides a potential mechanism to expand data access.

Although the academic and commercial sectors have developed successful methodologies for generating realistic synthetic EHR data, these...

United in Health / Unidos en Salud

BIDS Faculty Affiliate Maya Petersen is part of Unidos en Salud / United in Health, a collaboration of healthcare providers, infectious disease experts, community mobilizers, and people who are helping vulnerable populations through COVID-19.

COVID-19 disproportionately effects communities of color across the nation. In San Francisco, LatinX members make up roughly 50% of COVID-19 cases, despite being 15% of the...

Situational awareness dashboards for primary care clinicians

BIDS Health and Life Sciences Lead Maryam Vareth offers this project (#4) through UC Berkeley's Undergraduate Research Apprentice Program (URAP).

Situational awareness for primary care physicians plays an important role in providing proactive care for patients with complex health conditions. Many factors may impede physician’s situational awareness, particularly...

CRIC Cervix Collection

The CRIC Cervix Collection is a searchable image database — currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells — that makes digital cell image collections available for reproducible research and FAIR machine learning, with the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of...

Center for Recognition and Inspection of Cells (CRIC)

The Center for Recognition and Inspection of Cells (CRIC) uses massive databases of Pap smear images for the analysis and pre-screening of cervical cells. Our team is dedicated to research and development of software tools and organized cell image catalogues through automated cell morphometry and recognition using machine learning. Recent work shows advancements on cervical cell analysis of images from SUS (Brazilian Universal Health System), and human brain tissue and cells from Memory and Aging...