76 citations · 151 across the 18 of their papers we have counts for
5 papers · 1 filter
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…