9 citations · 11 across the 6 of their papers we have counts for
6 papers
DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem
Bruno A. Krinski, Daniel V. Ruiz, Rayson Laroca +1
Due to the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of semantic segmentation of Computed Tomography (C…
Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation
Bruno A. Krinski, Daniel V. Ruiz, Eduardo Todt
With the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of Semantic Segmentation of Computed Tomography (CT)…
Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation
Bruno A. Krinski, Daniel V. Ruiz, Eduardo Todt
With the COVID-19 global pandemic, computerassisted diagnoses of medical images have gained a lot of attention, and robust methods of Semantic Segmentation of Computed Tomography (…
IDA: Improved Data Augmentation Applied to Salient Object Detection
Daniel V. Ruiz, Bruno A. Krinski, Eduardo Todt
In this paper, we present an Improved Data Augmentation (IDA) technique focused on Salient Object Detection (SOD). Standard data augmentation techniques proposed in the literature,…
ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
Daniel V. Ruiz, Bruno A. Krinski, Eduardo Todt
In this paper, we propose a novel data augmentation technique (ANDA) applied to the Salient Object Detection (SOD) context. Standard data augmentation techniques proposed in the li…
Masking Salient Object Detection, a Mask Region-based Convolutional Neural Network Analysis for Segmentation of Salient Objects
Bruno A. Krinski, Daniel V. Ruiz, Guilherme Z. Machado +1
In this paper, we propose a broad comparison between Fully Convolutional Networks (FCNs) and Mask Region-based Convolutional Neural Networks (Mask-RCNNs) applied in the Salient Obj…