2 citations · 4 across the 4 of their papers we have counts for
4 papers
A Physics-Driven Neural Network with Parameter Embedding for Generating Quantitative MR Maps from Weighted Images
Lingjing Chen, Chengxiu Zhang, Yinqiao Yi +9
We propose a deep learning-based approach that integrates MRI sequence parameters to improve the accuracy and generalizability of quantitative image synthesis from clinical weighte…
ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning
Chenglong Wang, Yinqiao Yi, Yida Wang +5
With the ongoing development of deep learning, an increasing number of AI models have surpassed the performance levels of human clinical practitioners. However, the prevalence of A…
: A medical image annotation framework based on large vision model
Chenglong Wang, Dexuan Li, Sucheng Wang +4
Recently, large vision model, Segment Anything Model (SAM), has revolutionized the computer vision field, especially for image segmentation. SAM presented a new promptable segmenta…
Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis
Chenglong Wang, Yun Liu, Fen Wang +4
Lung cancer has the highest mortality rate of deadly cancers in the world. Early detection is essential to treatment of lung cancer. However, detection and accurate diagnosis of pu…