activity
20182025
most citedSwin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

915 citations · 1.2k across the 29 of their papers we have counts for

collaborators

29 papers

cs.CV2025★ 1 cited

Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision

Linhao Qu, Shiman Li, Xiaoyuan Luo +4

Computer-aided Whole Slide Image (WSI) classification has the potential to enhance the accuracy and efficiency of clinical pathological diagnosis. It is commonly formulated as a Mu…

cs.CV2024★ 1 cited

Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution

Minghong Duan, Linhao Qu, Shaolei Liu +1

Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR…

eess.IV2024★ 1 cited

An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution

Minghong Duan, Linhao Qu, Zhiwei Yang +3

High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinica…

cs.CV2023★ 2 cited

Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models

Shuo Wang, Yan Zhu, Xiaoyuan Luo +8

The development of artificial intelligence systems for colonoscopy analysis often necessitates expert-annotated image datasets. However, limitations in dataset size and diversity i…

cs.CV2023★ 1 cited

PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration

Mingzhi Yuan, Kexue Fu, Zhihao Li +2

Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications. Previous le…

cs.CV2023

OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification

Linhao Qu, Yingfan Ma, Zhiwei Yang +2

Active learning (AL) is an effective approach to select the most informative samples to label so as to reduce the annotation cost. Existing AL methods typically work under the clos…