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Found 8 papers, 3 papers with code
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation
Among different types of deep learning models, convolutional neural networks have been among the most successful and they have been used in many applications in medical imaging.
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation
Automatic segmentation of brain glioma from multimodal MRI scans plays a key role in clinical trials and practice.
A Question-Centric Model for Visual Question Answering in Medical Imaging
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks.
Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation
In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo-3D approach as a function of the number of input slices, and compare the results to conventional end-to-end 2D and 3D CNNs.
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging.
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks
Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord.
