5 papers
Forget or Fine-tune? A Comparative Study of Machine Unlearning Strategies for Noisy Label Correction
João L. P. Santana, Filipe R. Cordeiro
Noisy labels remain a critical challenge for training deep neural networks, since memorizing incorrect labels degrades generalization. Once noisy samples are identified after train…
Aycromo: An Open-Source Platform for Automatic Chromosome Detection in Metaphase Images Based on Deep Learning
Jorge L. A. Lima, Filipe R. Cordeiro
Chromosome analysis is a fundamental step in the diagnosis of genetic diseases, but the manual karyotyping workflow is time-consuming and heavily dependent on expert specialists, o…
Risk-Aware Robust Learning: Reducing Clinical Risk under Label Noise in Medical Image Classification
Maycon R. S. Pereira, Filipe R. Cordeiro
Noisy labels are a pervasive challenge in medical image classification, where annotation errors arise from inter-observer variability and diagnostic ambiguity. Although several noi…
Does Machine Unlearning Preserve Clinical Safety? A Risk Analysis for Medical Image Classification
Andreza M. C. Falcao, Filipe R. Cordeiro
The application of Deep Learning in medical diagnosis must balance patient safety with compliance with data protection regulations. Machine Unlearning enables the selective removal…
Data Augmentation Improves Machine Unlearning
Andreza M. C. Falcao, Filipe R. Cordeiro
Machine Unlearning (MU) aims to remove the influence of specific data from a trained model while preserving its performance on the remaining data. Although a few works suggest conn…