collaborators

7 papers

cs.CV2026

PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification

Larissa Ferreira Rodrigues Moreira, Leonardo Gabriel Ferreira Rodrigues, Rodrigo Moreira +1

Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytoplasmic appearance, which compli…

cs.LG2026

Asynchronous Probability Ensembling for Federated Disaster Detection

Emanuel Teixeira Martins, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira +3

Quick and accurate emergency handling in Disaster Decision Support Systems (DDSS) is often hampered by network latency and suboptimal application accuracy. While Federated Learning…

cs.DC2026

Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning

Victor Forattini Jansen, Emanuel Teixeira Martins, Yasmin Souza Lima +3

Convolutional Neural Networks (CNNs) have proven to be highly effective in solving a broad spectrum of computer vision tasks, such as classification, identification, and segmentati…

cs.CV2026

Exploiting Test-Time Augmentation in Federated Learning for Brain Tumor MRI Classification

Thamara Leandra de Deus Melo, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira +1

Efficient brain tumor diagnosis is crucial for early treatment; however, it is challenging because of lesion variability and image complexity. We evaluated convolutional neural net…

cs.CV2026

Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images

Elisa Gonçalves Ribeiro, Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira +1

Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings. Federated Learning (FL) mitigates this by keeping data local; however, its…

cs.CV2026

Energy-Aware Ensemble Learning for Coffee Leaf Disease Classification

Larissa Ferreira Rodrigues Moreira, Rodrigo Moreira, Leonardo Gabriel Ferreira Rodrigues

Coffee yields are contingent on the timely and accurate diagnosis of diseases; however, assessing leaf diseases in the field presents significant challenges. Although Artificial In…