Search Results for author: Eduardo Valle

Found 34 papers, 25 papers with code

DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion

no code implementations4 Sep 2023 Cédric Rommel, Eduardo Valle, Mickaël Chen, Souhaiel Khalfaoui, Renaud Marlet, Matthieu Cord, Patrick Pérez

We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE.

3D Human Pose Estimation

The Performance of Transferability Metrics does not Translate to Medical Tasks

1 code implementation14 Aug 2023 Levy Chaves, Alceu Bissoto, Eduardo Valle, Sandra Avila

Transfer learning boosts the performance of medical image analysis by enabling deep learning (DL) on small datasets through the knowledge acquired from large ones.

Transfer Learning

Test-Time Selection for Robust Skin Lesion Analysis

1 code implementation10 Aug 2023 Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila

Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information.


Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems

no code implementations22 Jun 2023 Juliano S. Assine, J. C. S. Santos Filho, Eduardo Valle, Marco Levorato

In approaches based on edge computing the execution of the models is offloaded to a compute-capable device positioned at the edge of 5G infrastructures.


Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues

no code implementations9 May 2023 Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila

Our protocol reveals three findings: 1) Models learn and propagate correlation shifts even with low-bias training; this poses a risk of accumulating and combining unaccountable weak biases; 2) Models learn robust features in high- and low-bias scenarios but use spurious ones if test samples have them; this suggests that spurious correlations do not impair the learning of robust features; 3) Diversity shift can reduce the reliance on spurious correlations; this is counter intuitive since we expect biased models to depend more on biases when invariant features are missing.


Artifact-Based Domain Generalization of Skin Lesion Models

1 code implementation20 Aug 2022 Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila

We propose a pipeline that relies on artifacts annotation to enable generalization evaluation and debiasing for the challenging skin lesion analysis context.

Domain Generalization Out-of-Distribution Generalization +1

A Survey on Deep Learning for Skin Lesion Segmentation

1 code implementation1 Jun 2022 Zahra Mirikharaji, Kumar Abhishek, Alceu Bissoto, Catarina Barata, Sandra Avila, Eduardo Valle, M. Emre Celebi, Ghassan Hamarneh

We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance).

Lesion Segmentation Segmentation +2

An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis

1 code implementation17 Jun 2021 Levy Chaves, Alceu Bissoto, Eduardo Valle, Sandra Avila

Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning.

Transfer Learning

Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation

2 code implementations3 May 2021 Juliano S. Assine, J. C. S. Santos Filho, Eduardo Valle

In the past few years, mobile deep-learning deployment progressed by leaps and bounds, but solutions still struggle to accommodate its severe and fluctuating operational restrictions, which include bandwidth, latency, computation, and energy.

object-detection Object Detection

GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review

1 code implementation20 Apr 2021 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.

Data Augmentation Test

Insights from the Future for Continual Learning

1 code implementation24 Jun 2020 Arthur Douillard, Eduardo Valle, Charles Ollion, Thomas Robert, Matthieu Cord

Continual learning aims to learn tasks sequentially, with (often severe) constraints on the storage of old learning samples, without suffering from catastrophic forgetting.

Class Incremental Learning Representation Learning +1

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

1 code implementation28 Apr 2020 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.

Experimental Design Lesion Classification +2

PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning

2 code implementations ECCV 2020 Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, Eduardo Valle

Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning.

Class Incremental Learning Incremental Learning +1

Debiasing Skin Lesion Datasets and Models? Not So Fast

1 code implementation23 Apr 2020 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.

Lesion Classification Skin Lesion Classification

Compressing Representations for Embedded Deep Learning

no code implementations23 Nov 2019 Juliano S. Assine, Alan Godoy, Eduardo Valle

Despite recent advances in architectures for mobile devices, deep learning computational requirements remains prohibitive for most embedded devices.

The Six Fronts of the Generative Adversarial Networks

no code implementations29 Oct 2019 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.

Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation

1 code implementation6 Jun 2019 Vinicius Ribeiro, Sandra Avila, Eduardo Valle

We also evaluate how conditioning the ground truths using different (but very simple) algorithms may help to enhance agreement and may be appropriate for some use cases.

Image Segmentation Lesion Classification +4

Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification

1 code implementation29 Apr 2019 Fábio Perez, Sandra Avila, Eduardo Valle

We evaluate that claim for melanoma classification, over 9 CNNs architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset, and 3 repeated measures, resulting in 135 models.

Classification General Classification +3

(De)Constructing Bias on Skin Lesion Datasets

1 code implementation18 Apr 2019 Alceu Bissoto, Michel Fornaciali, Eduardo Valle, Sandra Avila

We fed models with additional clinically meaningful information, which failed to improve the results even slightly, suggesting the destruction of cogent correlations.

BIG-bench Machine Learning

Data Augmentation for Skin Lesion Analysis

1 code implementation5 Sep 2018 Fábio Perez, Cristina Vasconcelos, Sandra Avila, Eduardo Valle

In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet).

Data Augmentation General Classification +3

Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018

no code implementations25 Aug 2018 Alceu Bissoto, Fábio Perez, Vinícius Ribeiro, Michel Fornaciali, Sandra Avila, Eduardo Valle

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).

Classification General Classification +3

Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis

1 code implementation1 Nov 2017 Eduardo Valle, Michel Fornaciali, Afonso Menegola, Julia Tavares, Flávia Vasques Bittencourt, Lin Tzy Li, Sandra Avila

We use the exhaustive trials to simulate sequential decisions and ensembles, with and without the use of privileged information from the test set.

Data Augmentation Test +1

Knowledge Transfer for Melanoma Screening with Deep Learning

2 code implementations22 Mar 2017 Afonso Menegola, Michel Fornaciali, Ramon Pires, Flávia Vasques Bittencourt, Sandra Avila, Eduardo Valle

Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening.

Image Classification Skin Cancer Classification +2

RECOD Titans at ISIC Challenge 2017

4 code implementations14 Mar 2017 Afonso Menegola, Julia Tavares, Michel Fornaciali, Lin Tzy Li, Sandra Avila, Eduardo Valle

This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma Detection" (ISBI 2017).

General Classification Lesion Segmentation +3

Known Unknowns: Uncertainty Quality in Bayesian Neural Networks

1 code implementation5 Dec 2016 Ramon Oliveira, Pedro Tabacof, Eduardo Valle

We compare the following candidate neural network models: Maximum Likelihood, Bayesian Dropout, OSBA, and --- for MNIST --- the standard variational approximation.

Anomaly Detection Known Unknowns

Adversarial Images for Variational Autoencoders

1 code implementation1 Dec 2016 Pedro Tabacof, Julia Tavares, Eduardo Valle

We find that autoencoders are much more robust to the attack than classifiers: while some examples have tolerably small input distortion, and reasonable similarity to the target image, there is a quasi-linear trade-off between those aims.

Adversarial Attack Test

Deep Neural Networks Under Stress

1 code implementation11 May 2016 Micael Carvalho, Matthieu Cord, Sandra Avila, Nicolas Thome, Eduardo Valle

In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets.

Transfer Learning

Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results

2 code implementations14 Apr 2016 Michel Fornaciali, Micael Carvalho, Flávia Vasques Bittencourt, Sandra Avila, Eduardo Valle

In this paper we survey, analyze and criticize current art on automated melanoma screening, reimplementing a baseline technique, and proposing two novel ones.

Melanoma Diagnosis

Semantic Diversity versus Visual Diversity in Visual Dictionaries

no code implementations20 Nov 2015 Otávio A. B. Penatti, Sandra Avila, Eduardo Valle, Ricardo da S. Torres

Results for image classification show that as visual dictionaries are based on low-level visual appearances, visual diversity is more important than semantic diversity.

General Classification Image Classification +1

Exploring the Space of Adversarial Images

2 code implementations19 Oct 2015 Pedro Tabacof, Eduardo Valle

Adversarial examples have raised questions regarding the robustness and security of deep neural networks.

Where Is My Puppy? Retrieving Lost Dogs by Facial Features

no code implementations9 Oct 2015 Thierry Pinheiro Moreira, Mauricio Lisboa Perez, Rafael de Oliveira Werneck, Eduardo Valle

Human facial recognizers perform poorly for dogs (up to 60. 5% accuracy), showing that dog facial recognition is not a trivial extension of human facial recognition.

Face Recognition

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