Publications (29)
HUR-MACL: High-Uncertainty Region-Guided Multi-Architecture Collaborative Learning for Head and Neck Multi-Organ Segmentation
Xiaoyu Liu, Siwen Wei, Linhao Qu +4
Accurate segmentation of organs at risk in the head and neck is essential for radiation therapy, yet deep learning models often fail on small, complexly shaped organs. While hybrid…
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…
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…
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…
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…
Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology
Minghao Han, Dingkang Yang, Linhao Qu +5
Recent years have witnessed remarkable progress in multimodal learning within computational pathology. Existing models primarily rely on vision and language modalities; however, la…