8 citations · 12 across the 7 of their papers we have counts for
7 papers
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…
Deep learning-based Segmentation of Rabbit fetal skull with limited and sub-optimal annotations
Rajath Soans, Alexa Gleason, Tosha Shah +4
In this paper, we propose a deep learning-based method to segment the skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which can assist in the assessment o…
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…