most citedGeneralist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

142 citations · 187 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023

CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading

Hao Wei, Peilun Shi, Juzheng Miao +5

Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a…

cs.CV2023142 cited

Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

Peilun Shi, Jianing Qiu, Sai Mu Dalike Abaxi +3

In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical ima…

cs.CV20231 cited

EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions

Peilun Shi, Jiachuan Peng, Jianing Qiu +3

Accurate depth estimation under adverse night conditions has practical impact and applications, such as on autonomous driving and rescue robots. In this work, we studied monocular…

cs.CV2022

Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning

Jiachuan Peng, Peilun Shi, Jianing Qiu +12

In our recent dietary assessment field studies on passive dietary monitoring in Ghana, we have collected over 250k in-the-wild images. The dataset is an ongoing effort to facilitat…

cs.CV2022

Tackling Long-Tailed Category Distribution Under Domain Shifts

Xiao Gu, Yao Guo, Zeju Li +5

Machine learning models fail to perform well on real-world applications when 1) the category distribution P(Y) of the training dataset suffers from long-tailed distribution and 2)…

cs.CV202244 cited

Mining Discriminative Food Regions for Accurate Food Recognition

Jianing Qiu, Frank P. -W. Lo, Yingnan Sun +2

Automatic food recognition is the very first step towards passive dietary monitoring. In this paper, we address the problem of food recognition by mining discriminative food region…