Publications (12)
Towards VQA Models That Can Read
Amanpreet Singh, Vivek Natarajan, Meet Shah +5
Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today's VQA models ca…
Beyond the Rubric: Cultural Misalignment in LLM Benchmarks for Sexual and Reproductive Health
Sumon Kanti Dey, Manvi S, Zeel Mehta +4
Large Language Models (LLMs) have been positioned as having the potential to expand access to health information in the Global South, yet their evaluation remains heavily dependent…
Cycle-Consistency for Robust Visual Question Answering
Meet Shah, Xinlei Chen, Marcus Rohrbach +1
Despite significant progress in Visual Question Answering over the years, robustness of today's VQA models leave much to be desired. We introduce a new evaluation protocol and asso…
A deep learning algorithm for reducing false positives in screening mammography
Stefano Pedemonte, Trevor Tsue, Brent Mombourquette +10
Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause…
Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks
Yash Bhalgat, Meet Shah, Suyash Awate
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in term…
A Hypersensitive Breast Cancer Detector
Stefano Pedemonte, Brent Mombourquette, Alexis Goh +6
Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population…