367 citations · 1.3k across the 29 of their papers we have counts for
30 papers
The Performance of Transferability Metrics does not Translate to Medical Tasks
Levy Chaves, Alceu Bissoto, Eduardo Valle +1
Transfer learning boosts the performance of medical image analysis by enabling deep learning (DL) on small datasets through the knowledge acquired from large ones. As the number of…
Test-Time Selection for Robust Skin Lesion Analysis
Alceu Bissoto, Catarina Barata, Eduardo Valle +1
Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information. Solutions that addr…
Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues
Alceu Bissoto, Catarina Barata, Eduardo Valle +1
Distribution shifts are common in real-world datasets and can affect the performance and reliability of deep learning models. In this paper, we study two types of distribution shif…
Artifact-Based Domain Generalization of Skin Lesion Models
Alceu Bissoto, Catarina Barata, Eduardo Valle +1
Deep Learning failure cases are abundant, particularly in the medical area. Recent studies in out-of-distribution generalization have advanced considerably on well-controlled synth…
A Survey on Deep Learning for Skin Lesion Segmentation
Zahra Mirikharaji, Kumar Abhishek, Alceu Bissoto +5
Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is a…
Seeing without Looking: Analysis Pipeline for Child Sexual Abuse Datasets
Camila Laranjeira, João Macedo, Sandra Avila +1
The online sharing and viewing of Child Sexual Abuse Material (CSAM) are growing fast, such that human experts can no longer handle the manual inspection. However, the automatic cl…
CIDEr-R: Robust Consensus-based Image Description Evaluation
Gabriel Oliveira dos Santos, Esther Luna Colombini, Sandra Avila
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly…
An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis
Levy Chaves, Alceu Bissoto, Eduardo Valle +1
Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning. By synthesizing annotations on pretext tasks, self-supervision…
GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review
Alceu Bissoto, Eduardo Valle, Sandra Avila
Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis. Genera…
#PraCegoVer: A Large Dataset for Image Captioning in Portuguese
Gabriel Oliveira dos Santos, Esther Luna Colombini, Sandra Avila
Automatically describing images using natural sentences is an important task to support visually impaired people's inclusion onto the Internet. It is still a big challenge that req…
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…
Debiasing Skin Lesion Datasets and Models? Not So Fast
Alceu Bissoto, Eduardo Valle, Sandra Avila
Data-driven models are now deployed in a plethora of real-world applications - including automated diagnosis - but models learned from data risk learning biases from that same data…
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…
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…
Grape detection, segmentation and tracking using deep neural networks and three-dimensional association
Thiago T. Santos, Leonardo L. de Souza, Andreza A. dos Santos +1
Agricultural applications such as yield prediction, precision agriculture and automated harvesting need systems able to infer the crop state from low-cost sensing devices. Proximal…
Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation
Vinicius Ribeiro, Sandra Avila, Eduardo Valle
In this work, we explore the issue of the inter-annotator agreement for training and evaluating automated segmentation of skin lesions. We explore what different degrees of agreeme…
Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification
Fábio Perez, Sandra Avila, Eduardo Valle
Convolutional neural networks (CNNs) deliver exceptional results for computer vision, including medical image analysis. With the growing number of available architectures, picking…
Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons
Akari Ishikawa, Edson Bollis, Sandra Avila
Watching cartoons can be useful for children's intellectual, social and emotional development. However, the most popular video sharing platform today provides many videos with Elsa…
(De)Constructing Bias on Skin Lesion Datasets
Alceu Bissoto, Michel Fornaciali, Eduardo Valle +1
Melanoma is the deadliest form of skin cancer. Automated skin lesion analysis plays an important role for early detection. Nowadays, the ISIC Archive and the Atlas of Dermoscopy da…
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…
Data Augmentation for Skin Lesion Analysis
Fábio Perez, Cristina Vasconcelos, Sandra Avila +1
Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin…
Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018
Alceu Bissoto, Fábio Perez, Vinícius Ribeiro +3
This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma Detection" (MICCAI 2018). Altho…
Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
Eduardo Valle, Michel Fornaciali, Afonso Menegola +4
Deep learning fostered a leap ahead in automated skin lesion analysis in the last two years. Those models are expensive to train and difficult to parameterize. Objective: We invest…
Knowledge Transfer for Melanoma Screening with Deep Learning
Afonso Menegola, Michel Fornaciali, Ramon Pires +3
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed fo…