1 citations · 1 across the 3 of their papers we have counts for
4 papers
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 (…
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…