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Found 6 papers, 5 papers with code
DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem
Our findings show that GAN-based techniques and spatial-level transformations are the most promising for improving the learning of deep models on this problem, with the StarGANv2 + F with a probability of 0. 3 achieving the highest F-score value on the Ricord1a dataset in the unified training strategy.
Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation
In this work, we propose an extensive analysis of how different data augmentation techniques improve the training of encoder-decoder neural networks on this problem.
Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation
To the best of our knowledge, this is the largest evaluation in number of encoders, decoders, and datasets proposed in the field of Covid-19 CT segmentation.
IDA: Improved Data Augmentation Applied to Salient Object Detection
Combining our method with others surpasses traditional techniques such as horizontal-flip in 0. 52% for F-measure and 1. 19% for Precision.
ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
We also compared our method with other data augmentation techniques.
Masking Salient Object Detection, a Mask Region-based Convolutional Neural Network Analysis for Segmentation of Salient Objects
However, there is no extensive comparison between the two networks in the SOD literature endorsing the effectiveness of Mask-RCNNs over FCN when segmenting salient objects.
