5 papers
Group Equivariant Generative Adversarial Networks
Neel Dey, Antong Chen, Soheil Ghafurian
Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perce…
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…
Restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks
Bairavi Venkatesh, Tosha Shah, Antong Chen +1
It is common for pathologists to annotate specific regions of the tissue, such as tumor, directly on the glass slide with markers. Although this practice was helpful prior to the a…
A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images
Dongqing Zhang, Ilknur Icke, Belma Dogdas +6
Automatic segmentation of left ventricle (LV) myocardium in cardiac short-axis cine MR images acquired on subjects with myocardial infarction is a challenging task, mainly because…
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…