1 code implementation • 10 Mar 2023 • Bruno A. Krinski, Daniel V. Ruiz, Rayson Laroca, Eduardo Todt
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.
1 code implementation • 19 May 2022 • Bruno A. Krinski, Daniel V. Ruiz, Eduardo Todt
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.
1 code implementation • 30 Sep 2021 • Bruno A. Krinski, Daniel V. Ruiz, Eduardo Todt
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.
1 code implementation • 18 Sep 2020 • Daniel V. Ruiz, Bruno A. Krinski, Eduardo Todt
Combining our method with others surpasses traditional techniques such as horizontal-flip in 0. 52% for F-measure and 1. 19% for Precision.
1 code implementation • 3 Oct 2019 • Daniel V. Ruiz, Bruno A. Krinski, Eduardo Todt
We also compared our method with other data augmentation techniques.
no code implementations • 17 Sep 2019 • Bruno A. Krinski, Daniel V. Ruiz, Guilherme Z. Machado, Eduardo Todt
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.