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Found 6 papers, 2 papers with code
Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation
The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches.
What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review
As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers).
Automatic code generation from sketches of mobile applications in end-user development using Deep Learning
A common need for mobile application development by end-users or in computing education is to transform a sketch of a user interface into wireframe code using App Inventor, a popular block-based programming environment.
Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data
One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles.
Road surface detection and differentiation considering surface damages
In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other information on other road surface that may be relevant to driving safety.
Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images
We achieved an overall IoU of 0. 78, showing the affordability of the approach of nuclei segmentation on Feulgen-stained images.
