Benchmarking PathCLIP for Pathology Image Analysis

no code implementations • 5 Jan 2024 • Sunyi Zheng, Xiaonan Cui, Yuxuan Sun, Jingxiong Li, Honglin Li, Yunlong Zhang, Pingyi Chen, Xueping Jing, Zhaoxiang Ye, Lin Yang

Additionally, we assess the robustness of PathCLIP in the task of image-image retrieval, revealing that PathCLIP performs less effectively than PLIP on Osteosarcoma but performs better on WSSS4LUAD under diverse corruptions.

Benchmarking Decision Making +4

Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification

no code implementations • 13 Jan 2020 • Sunyi Zheng, Ludo J. Cornelissen, Xiaonan Cui, Xueping Jing, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M. A. van Ooijen

Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94. 2% with 1. 0 false positive/scan and a sensitivity of 96. 0% with 2. 0 false positives/scan.

Lung Nodule Detection

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

no code implementations • 11 Apr 2019 • Sunyi Zheng, Jiapan Guo, Xiaonan Cui, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M. A. van Ooijen

Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans.

Computed Tomography (CT) Lung Nodule Detection