3 papers
eess.IV2023
Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations
Shaoyan Pan, Yiqiao Liu, Sarah Halek +6
In oncology research, accurate 3D segmentation of lesions from CT scans is essential for the modeling of lesion growth kinetics. However, following the RECIST criteria, radiologist…
eess.IV2020
A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
Antong Chen, Jennifer Saouaf, Bo Zhou +6
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cel…
cs.CV2018
A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
Bo Zhou, Randolph Crawford, Belma Dogdas +2
Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic…