Published in arXiv, 2022
Experiments and results from early 2021 - This paper compare Object detection and semantic segmentation approaches to identify the nuclei and they nucleolus in AgNOR-stained cytology images. Applied into CCAgT dataset v1.
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Published in Mendelay Data, 2022
Published in International Journal of Remote Sensing, 2022
A paper which compare machine learning models to compute wheat biomass with UAV-based multispectral Images. Just as we made public the Dataset: UAV-based multispectral images of spring wheat.
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Published in International Geoscience and Remote Sensing Symposium, 2022
This library is capable of automatically classifying the risk of a landslide occurrence in near realtime, as well as in terms of prediction (future), and abstracting the information in a summarized manner for the end user.
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Published in Mendelay Data, 2022
Published in SSRN - Social Science Research Network, 2022
This paper compare different semantic segmentation model to detect the very small structures into AgNOR-Stained Cytology Images. Applied into CCAgT dataset v2.
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Published in SSRN - Social Science Research Network, 2022
Using UFSC OCPap v4.
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Published in Mendelay Data, 2022
Published in Computer on the Beach 2022 - COPT XIII, 2022
This paper apply object detection into AgNOR-Stained Cytology Images. Differentiate between different categories of nuclei - at CCAgT dataset v1.5.
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Published in Applied Artificial Intelligence, 2022
A paper which compare CNN and ANN to compute wheat biomass with UAV-based RGB Images. Just as we made public the Brazilian Wheat Dataset.
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Published in Cold Spring Harbor Laboratory, 2021
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Published in Computerized Medical Imaging and Graphics, 2021
A Review of the state-of-the-art of Classic Computer Vision and Deep Learning-Based Approaches applied into cytology.
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Published in SN Computer Science, 2021
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Published in XVII Congresso Brasileiro de Informática em saúde - CBIS 2020, 2020
This paper apply object detection into AgNOR-Stained Cytology Images. Nuclei detection (binary) - at CCAgT dataset v1.
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Published in XVII Congresso Brasileiro de Informática em saúde - CBIS 2020, 2020
This paper compare object detection and semantic segmentation into oral cytology samples stained with papanicolaou. Nuclei detection (binary) - at UFSC OCPap dataset v1.
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Published in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020
This paper apply semantic segmentation into AgNOR-Stained Cytology Images. There is also the publication of the first version (v1) of CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique.
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Published in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020
This paper apply segmentation, detection and classification of cell nuclei into oral cytology samples stained with papanicolaou. There is also the publication of the first version (v1) of UFSC OCPap dataset: Papanicolaou Stained Oral Cytology Dataset.
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Published in Federal University of Santa Catarina, 2020
A Review of Classic Computer Vision and Deep Learning-Based Approaches of computer vision for cytology.
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