8 papers · 1 filter
A self-supervised learning approach to deep filter banks for texture recognition
Joao B. Florindo, Lucas O. Lyra, Antonio E. Fabris
An important challenge in texture recognition is the limited amount of data for training frequently found in real-world applications. In computer vision in general, a successful st…
Sleep-stage efficient classification using a lightweight self-supervised model
Eldiane Borges dos Santos Durães, João Batista Florindo
Accurate classification of sleep stages is crucial for diagnosing sleep disorders and automating this process can significantly enhance clinical assessments. This study aims to exp…
A multifractal-based masked auto-encoder: an application to medical images
Joao Batista Florindo, Viviane de Moura
Masked autoencoders (MAE) have shown great promise in medical image classification. However, the random masking strategy employed by traditional MAEs may overlook critical areas in…
ConvNeXt-FD: A Fractal-Based Deep Model for Robust Biomedical Image Segmentation
Joao Batista Florindo, Amanda Pontes de Oliveira Ornelas
Biomedical image segmentation is a critical task in medical diagnosis and treatment planning, enabling precise delineation of anatomical structures and pathological regions. Despit…
Chaotic Contrastive Learning for Robust Texture Classification
Joao B Florindo
Texture classification is a pivotal task in computer vision, presenting unique challenges due to high inter-class similarity and the sensitivity of structural patterns to scale and…
Attention-Based Chaotic Self-Supervision for Medical Image Classification
Joao Batista Florindo, Amanda Pontes de Oliveira Ornelas
Deep learning models for medical image classification usually achieve promising results but typically rely on large, annotated datasets or standard transfer learning from ImageNet.…