activity
20162020
most citedSkin Lesion Synthesis with Generative Adversarial Networks

105 citations · 154 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2020

Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation

Vinicius Ribeiro, Sandra Avila, Eduardo Valle

Segmenting skin lesions images is relevant both for itself and for assisting in lesion classification, but suffers from the challenge in obtaining annotated data. In this work, we…

cs.CV2020

Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmark

Edson Bollis, Helio Pedrini, Sandra Avila

Pests and diseases are relevant factors for production losses in agriculture and, therefore, promote a huge investment in the prevention and detection of its causative agents. In m…

cs.CV201915 cited

The Six Fronts of the Generative Adversarial Networks

Alceu Bissoto, Eduardo Valle, Sandra Avila

Generative Adversarial Networks fostered a newfound interest in generative models, resulting in a swelling wave of new works that new-coming researchers may find formidable to surf…

cs.CV2019105 cited

Skin Lesion Synthesis with Generative Adversarial Networks

Alceu Bissoto, Fábio Perez, Eduardo Valle +1

Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly. Currently, Deep Neural Networks are…

cs.CV201634 cited

Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes

Afonso Menegola, Michel Fornaciali, Ramon Pires +2

Deep learning is the current bet for image classification. Its greed for huge amounts of annotated data limits its usage in medical imaging context. In this scenario transfer learn…