6 papers
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation
Xiaochuan Ma, Jia Fu, Wenjun Liao +2
Brain tumor segmentation is important for diagnosis of the tumor, and current deep-learning methods rely on a large set of annotated images for training, with high annotation costs…
Large-scale cervical precancerous screening via AI-assisted cytology whole slide image analysis
Honglin Li, Yusuan Sun, Chenglu Zhu +8
Cervical Cancer continues to be the leading gynecological malignancy, posing a persistent threat to women's health on a global scale. Early screening via cytology Whole Slide Image…
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Zixia Jia, Junpeng Li, Shichuan Zhang +2
Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like d…
Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis
Shichuan Zhang, Sunyi Zheng, Zhongyi Shui +2
Multi-modal Learning has attracted widespread attention in medical image analysis. Using multi-modal data, whole slide images (WSIs) and clinical information, can improve the perfo…
Exploring Unsupervised Cell Recognition with Prior Self-activation Maps
Pingyi Chen, Chenglu Zhu, Zhongyi Shui +4
The success of supervised deep learning models on cell recognition tasks relies on detailed annotations. Many previous works have managed to reduce the dependency on labels. Howeve…
End-to-end cell recognition by point annotation
Zhongyi Shui, Shichuan Zhang, Chenglu Zhu +4
Reliable quantitative analysis of immunohistochemical staining images requires accurate and robust cell detection and classification. Recent weakly-supervised methods usually estim…