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Found 7 papers, 0 papers with code
Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations
In oncology research, accurate 3D segmentation of lesions from CT scans is essential for the modeling of lesion growth kinetics.
Deep learning-based Segmentation of Rabbit fetal skull with limited and sub-optimal annotations
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 of drug-induced skeletal abnormalities as a required study in developmental and reproductive toxicology (DART).
Group Equivariant Generative Adversarial Networks
Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity.
A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
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 cell lung cancer trials.
Restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks
We train our network on up to 300 whole slide images with marker inks and show that 70% of the corrected image patches are indistinguishable from originally uncontaminated image tissue to a human expert.
Generative Adversarial Network
Image-to-Image Translation
+2
A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images
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 of the various types of image inhomogeneity caused by the infarctions.
A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST), clinicians typically outline the boundaries of a lesion on a single slice to extract diameter measurements.
