activity
20222024
most citedTask-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification

6 citations · 11 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2024

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…

cs.CV2024

WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering

Pingyi Chen, Chenglu Zhu, Sunyi Zheng +2

Whole slide imaging is routinely adopted for carcinoma diagnosis and prognosis. Abundant experience is required for pathologists to achieve accurate and reliable diagnostic results…

eess.IV2024

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…

cs.CV20243 cited

PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology

Yuxuan Sun, Hao Wu, Chenglu Zhu +11

The emergence of large multimodal models has unlocked remarkable potential in AI, particularly in pathology. However, the lack of specialized, high-quality benchmark impeded their…

cs.CV20231 cited

Test-Time Training for Semantic Segmentation with Output Contrastive Loss

Yunlong Zhang, Yuxuan Sun, Sunyi Zheng +3

Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge. To i…

cs.CV2023

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…