Publications (6)
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
Emma Chen, Andy Kim, Rayan Krishnan +3
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work,…
Machine Learning Sensors
Pete Warden, Matthew Stewart, Brian Plancher +6
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from c…
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Emma Chen, Aman Kansal, Julie Chen +4
We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+…
FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models
Alice Heiman, Xiaoman Zhang, Emma Chen +2
Medical vision-language models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability…
Slm-mux: Orchestrating small language models for reasoning
Chenyu Wang, Zishen Wan, Hao Kang +5
With the rapid development of language models, the number of small language models (SLMs) has grown significantly. Although they do not achieve state-of-the-art accuracy, they are…
Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge
Shvetank Prakash, Andrew Cheng, Olof Kindgren +13
We present FlexiFlow, a lifetime-aware design framework for item-level intelligence (ILI) where computation is integrated directly into disposable products like food packaging and…