390 citations · 953 across the 27 of their papers we have counts for
27 papers
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection
Matheus A. Cerqueira, Flávia Sprenger, Bernardo C. A. Teixeira +1
Brain tumor image segmentation is a challenging research topic in which deep-learning models have presented the best results. However, the traditional way of training those models…
A Practical Algorithm for Max-Norm Optimal Binary Labeling of Graphs
Filip Malmberg, Alexandre X. Falcão
This paper concerns the efficient implementation of a method for optimal binary labeling of graph vertices, originally proposed by Malmberg and Ciesielski (2020). This method finds…
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning
Bárbara Caroline Benato, Alexandre Xavier Falcão, Alexandru-Cristian Telea
Lacking supervised data is an issue while training deep neural networks (DNNs), mainly when considering medical and biological data where supervision is expensive. Recently, Embedd…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Efficient Multiscale Object-based Superpixel Framework
Felipe Belém, Benjamin Perret, Jean Cousty +2
Superpixel segmentation can be used as an intermediary step in many applications, often to improve object delineation and reduce computer workload. However, classical methods do no…
Iterative Saliency Enhancement using Superpixel Similarity
Leonardo de Melo Joao, Alexandre Xavier Falcao
Saliency Object Detection (SOD) has several applications in image analysis. The methods have evolved from image-intrinsic to object-inspired (deep-learning-based) models. When a mo…