76 citations · 151 across the 18 of their papers we have counts for
18 papers
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…
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…
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
Kexue Fu, Xiaoyuan Luo, Linhao Qu +5
The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification…
Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation
Xiaoyu Liu, Linhao Qu, Ziyue Xie +2
The task of labeling multiple organs for segmentation is a complex and time-consuming process, resulting in a scarcity of comprehensively labeled multi-organ datasets while the eme…
Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations
Dingkang Yang, Mingcheng Li, Linhao Qu +4
Understanding human intentions (e.g., emotions) from videos has received considerable attention recently. Video streams generally constitute a blend of temporal data stemming from…
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
Zhiwei Yang, Kexue Fu, Minghong Duan +3
Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks without dense annotations. However, attributed to the frequent coupling of…
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…
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…
Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation
Shaolei Liu, Siqi Yin, Linhao Qu +1
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing…
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…