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W. Peng

4 papers hereh-index 5161 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author1

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • eess.IV2
same name
  • W. Peng — 24 papers, h 19
  • W. Peng — 19 papers, h 24
  • W. Peng — 8 papers, h 10
  • W. Peng — 6 papers, h 3
  • W. Peng — 5 papers, h 3
  • W. Peng — 4 papers, h 21

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232025
collaborators

4 papers

cs.CV2025

Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining

Wonhyeok Choi, Kyumin Hwang, Wei Peng +2

Self-supervised monocular depth estimation (SSMDE) aims to predict the dense depth map of a monocular image, by learning depth from RGB image sequences, eliminating the need for gr…

eess.IV2024

LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models

Hantao Zhang, Yuhe Liu, Jiancheng Yang +4

Patient data from real-world clinical practice often suffers from data scarcity and long-tail imbalances, leading to biased outcomes or algorithmic unfairness. This study addresses…

cs.CV2023

Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

Mohammed Baharoon, Waseem Qureshi, Jiahong Ouyang +3

The integration of deep learning systems into healthcare has been hindered by the resource-intensive process of data annotation and the inability of these systems to generalize to…

eess.IV2023

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

Yanwu Xu, Li Sun, Wei Peng +7

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly…

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