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Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Zhihao Li, Jiancheng Yang, Yongchao Xu +3
Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based…
The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases
Jiancheng Yang, Hongwei Bran Li, Donglai Wei
This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging. With the aid of public data, these models, which p…
Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset
Ziqiao Weng, Jiancheng Yang, Dongnan Liu +1
Accurate segmentation of pulmonary airways and vessels is crucial for the diagnosis and treatment of pulmonary diseases. However, current deep learning approaches suffer from disco…
SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention
Rui Xu, Zhi Liu, Yong Luo +5
Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished…
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Liang Jin, Shixuan Gu, Donglai Wei +7
Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccess…
LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection
Rui Xu, Yong Luo, Bo Du +2
Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detectio…
What Makes for Automatic Reconstruction of Pulmonary Segments
Kaiming Kuang, Li Zhang, Jingyu Li +4
3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure…
Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision
Dingyi Rong, Jiancheng Yang, Bingbing Ni +1
Projection map (PM) from optical coherence tomography (OCT) B-scan is an important tool to diagnose retinal diseases, which typically requires retinal layer segmentation. In this s…
RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans
Jiancheng Yang, Shixuan Gu, Donglai Wei +2
Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib se…
Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study
Jiancheng Yang, Mingze Gao, Kaiming Kuang +4
Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved g…
Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules
Yamin Li, Jiancheng Yang, Yi Xu +5
Follow-up serves an important role in the management of pulmonary nodules for lung cancer. Imaging diagnostic guidelines with expert consensus have been made to help radiologists m…
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers
Jiancheng Yang, Haoran Deng, Xiaoyang Huang +2
Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple…
Reinventing 2D Convolutions for 3D Images
Jiancheng Yang, Xiaoyang Huang, Yi He +4
There have been considerable debates over 2D and 3D representation learning on 3D medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they are gene…
Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis
Jiancheng Yang, Rongyao Fang, Bingbing Ni +3
Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emer…