activity
20182026
most citedTowards 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

76 citations · 192 across the 18 of their papers we have counts for

collaborators

21 papers

cs.CV2026

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…

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

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…

cs.CV2023★ 4 cited

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…

cs.CV2023★ 13 cited

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…