Search Results for author:
Found 6 papers, 4 papers with code
Extending 3D body pose estimation for robotic-assistive therapies of autistic children
In real-world settings, the proposed model performs similarly to a Kinect depth camera and manages to successfully estimate the 3D body poses in a much higher number of frames.
XAI for Skin Cancer Detection with Prototypes and Non-Expert Supervision
Skin cancer detection through dermoscopy image analysis is a critical task.
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).
