76 citations · 211 across the 21 of their papers we have counts for
21 papers
Less Is More in the Long Tail: Stage-Adaptive Sample Selection for Annotation-Efficient Dense Prediction
Xiaofei Du, Lei Zhang, Shuyu Yan +2
Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-scale dense prediction tasks w…
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…
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…
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…
Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need
Linhao Qu, Yingfan Ma, Xiaoyuan Luo +2
Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut ou…
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
Linhao Qu, Xiaoyuan Luo, Kexue Fu +2
This paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed based o…
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Linhao Qu, Minghong Duan, Zhiwei Yang +2
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have…
Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation
Xiaoyu Liu, Linhao Qu, Ziyue Xie +3
Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiat…
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation
Shiman Li, Haoran Wang, Yucong Meng +2
Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-gui…
EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)
Haoran Wang, Yan Zhu, Wenzheng Qin +5
The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets…
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Linhao Qu, Xiaoyuan Luo, Manning Wang +1
Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-super…
Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis
Linhao Qu, Siyu Liu, Xiaoyu Liu +2
Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of p…
DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification
Linhao Qu, Xiaoyuan Luo, Shaolei Liu +2
Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distributio…
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning
Linhao Qu, Shaolei Liu, Manning Wang +4
Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Due to insufficient task-…
TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning
Linhao Qu, Shaolei Liu, Manning Wang +1
In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-de…
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds
Xiaoyuan Luo, Shaolei Liu, Kexue Fu +2
Deep neural networks have achieved promising performance in supervised point cloud applications, but manual annotation is extremely expensive and time-consuming in supervised learn…
WaveFuse: A Unified Deep Framework for Image Fusion with Discrete Wavelet Transform
Shaolei Liu, Manning Wang, Zhijian Song
We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy a…
Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling
Chunli Qin, Demin Yao, Han Zhuang +3
Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment. Existing methods ar…
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
Yinlong Liu, Xuechen Li, Manning Wang +3
Absolute pose estimation is a fundamental problem in computer vision, and it is a typical parameter estimation problem, meaning that efforts to solve it will always suffer from out…
Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition
Xuechen Li, Yinlong Liu, Yiru Wang +3
The rigid registration of two 3D point sets is a fundamental problem in computer vision. The current trend is to solve this problem globally using the BnB optimization framework. H…
Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net
Yueyue Wang, Liang Zhao, Zhijian Song +1
Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is c…