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eess.IV2024

Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation

Amir Syahmi, Xiangrong Lu, Yinxuan Li +7

Recent advancements in medical imaging and artificial intelligence (AI) have greatly enhanced diagnostic capabilities, but the development of effective deep learning (DL) models is…

eess.IV2024

Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method

Shiyi Wang, Yang Nan, Sheng Zhang +6

In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation. Additionally, Deep Learning (DL) methods face challenges: the opaci…

eess.IV2024

Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation

Sheng Zhang, Yang Nan, Yingying Fang +4

Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs can lead to false-negat…

eess.IV2023

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

Yang Nan, Xiaodan Xing, Shiyi Wang +38

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains…

eess.IV2023

High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation

Shiyi Wang, Yang Nan, Simon Walsh +1

We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmen…