activity
20192026
most citedBuilding Damage Detection in Satellite Imagery Using Convolutional Neural Networks

116 citations · 145 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Towards Expert-level Medical AI for Real-time Video Consultations

Mahvish Nagda, Jihyeon Lee, Matthew Thompson +37

Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues. While text…

cs.HC2026★ 1 cited

A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

Peter Brodeur, Jacob M. Koshy, Anil Palepu +45

Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clin…

cs.CV2024★ 1 cited

Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data

Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn +4

We present a simple and efficient method to leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from a…

cs.CV2020★ 27 cited

Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques

Jihyeon Lee, Joseph Z. Xu, Kihyuk Sohn +8

To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which in…

cs.CV2019★ 116 cited

Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks

Joseph Z. Xu, Wenhan Lu, Zebo Li +2

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effect…