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20222025
most citedBi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification

40 citations · 47 across the 9 of their papers we have counts for

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12 papers · 1 filter

cs.CV20251 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.CV20241 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…

cs.CV20241 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV20242 cited

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…