activity
20172022
most citedRhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation

421 citations · 493 across the 9 of their papers we have counts for

collaborators

16 papers

cs.CV2021

Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image

Yuanqi Du, Quan Quan, Hu Han +1

Pseudo-normality synthesis, which computationally generates a pseudo-normal image from an abnormal one (e.g., with lesions), is critical in many perspectives, from lesion detection…

cs.CV2021

Conditional Training with Bounding Map for Universal Lesion Detection

Han Li, Long Chen, Hu Han +1

Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by coarse-to-fine two-stage de…

cs.CV20207 cited

Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models

Pengbo Liu, Hu Han, Yuanqi Du +9

Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segme…

cs.CV2020

Bounding Maps for Universal Lesion Detection

Han Li, Hu Han, S. Kevin Zhou

Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis systems. Many detection approaches achieve excellent results for ULD usi…

cs.CV202011 cited

Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling

Xuesong Niu, Zitong Yu, Hu Han +3

Remote physiological measurements, e.g., remote photoplethysmography (rPPG) based heart rate (HR), heart rate variability (HRV) and respiration frequency (RF) measuring, are playin…

cs.CV20202 cited

Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection

Qingsong Yao, Zecheng He, Hu Han +1

Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnera…