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04/20/2021 - 08:54

Congratulations to Josh Sanyal, a high school student in the lab who won 3rd Place in Math and Computer Science at the 2021 Northern California Junior Science & Humanities Symposium (JSHS) for his Poster Presentation,  “A Weakly Supervised LSTM Model for Longitudinal Breast Cancer Recurrence Prediction via Unstructured Clinical Narratives.” Press release: https://docs.google.com/document/d/1Jwa5t6qvk-EYdtxcOM033lvXuXT62AAS0pnE8ZA7H7c/edit 

03/29/2021 - 08:47

We are delighted to announce that Isabel Gallegos, a Stanford undergraduate student in the QIAI lab has been named a 2021 Goldwater Scholar for her #AI proposal, “Use biomedical data science to build machine learning models to improve healthcare decision-making.”

03/27/2021 - 08:28

We are delighted to announce that Juan Manuel Zambrano Chaves, a PhD student in the Rubin QIAI lab, won Best Technology Paper, with his paper, Zambrano Chaves JM, Chaudhari AS, Wentland AL, Desai AD, Banerjee I, Boutin RD, Maron DJ, Rodriguez F, Sandhu AT, Jeffrey RB, Rubin DL, Patel B, "Opportunistic Screening for Ischemic Heart Disease Risk Using Abdominopelvic Computed Tomography and Medical Record Data: A Multimodal Explainable Artificial Intelligence Approach," at the Society of Abdominal Radiology meeting, 2021.

 

03/27/2021 - 08:26

We are delighted and proud to announce that our Mentored High School Student , Snikitha Banda, was the Grand Prize Award Winner, for his paper “Style Transfer Augmentation: Cancer Subtype Classification using Genetic Status in Histology,” and now Finalist in the Regeneron ISEF 2021 and the 2021 Synopsys (Santa Clara Valley Science and Engineering Fair Association-SCVSEFA,

03/27/2021 - 08:24

We are delighted to announce that our Mentored High School Student in Rubin Lab, Claire Tang, won top 40 finalist, “Automated Diagnostic Imaging for COVID-19: from the Unknown Detection to Clinical Prediction,” in the international 80th Regeneron Science Talent Search, 2021.

03/27/2021 - 08:23

Our Mentored High School Student , Aalok Patwa, won top 300 scholar, “Analysis of the Tumor-Immune Microenvironment Reveals Predictors of Recurrence and Overall Survival in Triple-Negative Breast Cancer,” in the 80th Regeneron Science Talent Search, 2021.

03/27/2021 - 08:22

Our Mentored High School Student , Siddharth Sharma, won top 300 scholar for his paper, “Nature’s Learning Algorithm? Experiments and Analysis of the Hebbian-LMS Algorithm,” 80th Regeneron Science Talent Search, 2021.

 

03/27/2021 - 08:20

Our poster, Chang K, et al., and Rubin DL and Kalpathy-Cramer J, Federated Deep Learning Among Multiple Institutions for Automated Classification of Breast Density, won Second Prize, 2020 NIH ITCR Poster Competition.

03/27/2021 - 08:19

We are pleased to announce that our paper, Banerjee I Bozkurt S,-Jin JLC,. Kurian AW, Rubin DL, Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer, JCO Clin Cancer Inform 3:1-12, 2019  was named One of the 15 best papers in Cancer informatics 2019 selected by Yearbook of Medical Informatics (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442504/).

06/22/2019 - 09:52

We are pleased to announce that the QIAI Lab has been awarded a second grant from the NIH, a U01 under NCI’s Informatics Technologies for Cancer Research and Management (ITCR) program. The grant entitled “Distributed computation of predictive models for precision medicine in cancer” will focus on developing methods and tools for training AI models relevant to cancer through federated computational methods that do not require any data sharing and that will propel multi-institutional collaborations for creating more robust AI models. The ITRC program is a very large network of centers developing enabling data science technologies to advance cancer discovery, and the QIAI Lab's involvement in this network will permit it to have a voice in developing emerging standardized approaches to data analysis in cancer problems as well as to deliver enabling technologies to the community that will accelerate creation of AI applications.

06/22/2019 - 09:47

We are please to announce that the QIAI Lab is part of a team that was awarded a U24 grant under NCI’s Oncology Co-Clinical Imaging Research Resources to Encourage Consensus on Quantitative Imaging Methods and Precision Medicine, which is a new network of sites developing innovative methods to enable co-clinical (animal/human) trials. The grant is entitled “Integrating omics and quantitative imaging data in co-clinical trials to predict treatment response in triple negative breast cancer.” The ePAD technology (http://epad.stanford.edu) developed by the QIAI Lab is central to this grant by bringing quantitative image analysis methods to small animal imaging and unifying quantitative assessment of cancer on images in both animal and human studies to catalyze research in the co-clinical trials paradigm.

04/08/2019 - 10:57

We are pleased to announce that ePAD project (http://epad.stanford.edu) of the QIAI Lab, "ePAD: A platform to enable machine learning and AI application development in medical imaging" (B-0906) has been selected to receive the Best Scientific Paper Presentation Award at the 2019 European Congress of Radiology in Imaging Informatics, https://www.myesr.org/congress

09/22/2018 - 18:21

We have recently developed methods for distributed computation of deep learning models in JAMIA. This may break the barrier to leveraging multi-institutional data and enable creating more robust deep learning models. Check it out

04/26/2017 - 11:21

On May 10, 2017, Dr. Assaf Hoogi, a Postdoctoral Scholar in the Dr. Rubin's laboratory received the prestigeous Larry Clarke QIN Young Investigator Travel Award for is outstanding and innovative work in adaptive methods for image segmentation and career vision for advancing quantitative imaging.

04/26/2017 - 11:18

A scientific paper by Zeshan Hussain (Graduate Student in Dr. Rubin's laboratory) and Dr. Francisco Gimenez (recently graduated from Dr. Rubin's laboratory) was selected for a spotlight talk at NIPS 2016 (http://www.nipsml4hc.ws/), entitled, "Differential Data Augmentation Techniques for Medical Imaging Classification Tasks"