192 citations · 350 across the 16 of their papers we have counts for
17 papers
Label-free estimation of clinically relevant performance metrics under distribution shifts
Tim Flühmann, Alceu Bissoto, Trung-Dung Hoang +1
Performance monitoring is essential for safe clinical deployment of image classification models. However, because ground-truth labels are typically unavailable in the target datase…
A Real-Time Digital Twin for Type 1 Diabetes using Simulation-Based Inference
Trung-Dung Hoang, Alceu Bissoto, Vihangkumar V. Naik +4
Accurately estimating parameters of physiological models is essential to achieving reliable digital twins. For Type 1 Diabetes, this is particularly challenging due to the complexi…
Subgroup Performance Analysis in Hidden Stratifications
Alceu Bissoto, Trung-Dung Hoang, Tim Flühmann +3
Machine learning (ML) models may suffer from significant performance disparities between patient groups. Identifying such disparities by monitoring performance at a granular level…
Are Explanations Helpful? A Comparative Analysis of Explainability Methods in Skin Lesion Classifiers
Rosa Y. G. Paccotacya-Yanque, Alceu Bissoto, Sandra Avila
Deep Learning has shown outstanding results in computer vision tasks; healthcare is no exception. However, there is no straightforward way to expose the decision-making process of…
BiasPruner: Debiased Continual Learning for Medical Image Classification
Nourhan Bayasi, Jamil Fayyad, Alceu Bissoto +2
Continual Learning (CL) is crucial for enabling networks to dynamically adapt as they learn new tasks sequentially, accommodating new data and classes without catastrophic forgetti…
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…
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…
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…
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…
(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…
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…