Search Results for author: Bruno A. Krinski

Found 6 papers, 5 papers with code

DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem

1 code implementation10 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.

Computed Tomography (CT) Data Augmentation +1

Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation

1 code implementation19 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.

Computed Tomography (CT) Data Augmentation +1

Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation

1 code implementation30 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.

Computed Tomography (CT) Semantic Segmentation

IDA: Improved Data Augmentation Applied to Salient Object Detection

1 code implementation18 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.

Data Augmentation Image Cropping +4

Masking Salient Object Detection, a Mask Region-based Convolutional Neural Network Analysis for Segmentation of Salient Objects

no code implementations17 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.

object-detection RGB Salient Object Detection +1

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