4 papers
FLIM Networks with Bag of Feature Points
João Deltregia Martinelli, Marcelo Luis Rodrigues Filho, Felipe Crispim da Rocha Salvagnini +3
Convolutional networks require extensive image annotation, which can be costly and time-consuming. Feature Learning from Image Markers (FLIM) tackles this challenge by estimating e…
Multi-level Cellular Automata for FLIM networks
Felipe Crispim Salvagnini, Jancarlo F. Gomes, Cid A. N. Santos +2
The necessity of abundant annotated data and complex network architectures presents a significant challenge in deep-learning Salient Object Detection (deep SOD) and across the broa…
Flyweight FLIM Networks for Salient Object Detection in Biomedical Images
Leonardo M. Joao, Jancarlo F. Gomes, Silvio J. F. Guimaraes +2
Salient Object Detection (SOD) with deep learning often requires substantial computational resources and large annotated datasets, making it impractical for resource-constrained ap…
Self-calibrated convolution towards glioma segmentation
Felipe C. R. Salvagnini, Gerson O. Barbosa, Alexandre X. Falcao +1
Accurate brain tumor segmentation in the early stages of the disease is crucial for the treatment's effectiveness, avoiding exhaustive visual inspection of a qualified specialist o…