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Found 13 papers, 10 papers with code
XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision
Skin cancer detection through dermoscopy image analysis is a critical task.
The Performance of Transferability Metrics does not Translate to Medical Tasks
Transfer learning boosts the performance of medical image analysis by enabling deep learning (DL) on small datasets through the knowledge acquired from large ones.
Test-Time Selection for Robust Skin Lesion Analysis
Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information.
Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues
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
We propose a pipeline that relies on artifacts annotation to enable generalization evaluation and debiasing for the challenging skin lesion analysis context.
A Survey on Deep Learning for Skin Lesion Segmentation
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).
An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis
Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning.
GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review
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.
Debiasing Skin Lesion Datasets and Models? Not So Fast
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
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
We fed models with additional clinically meaningful information, which failed to improve the results even slightly, suggesting the destruction of cogent correlations.
Skin Lesion Synthesis with Generative Adversarial Networks
Skin cancer is by far the most common type of cancer.
Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018
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).
