1 citations · 1 across the 4 of their papers we have counts for
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Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift
Akshit Achara, Yovin Yahathugoda, Nick Byrne +4
The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A common way to test for such…
Confidence Matters: Uncertainty Quantification and Precision Assessment of Deep Learning-based CMR Biomarker Estimates Using Scan-rescan Data
Dewmini Hasara Wickremasinghe, Michelle Gibogwe, Andrew Bell +6
The performance of deep learning (DL) methods for the analysis of cine cardiovascular magnetic resonance (CMR) is typically assessed in terms of accuracy, overlooking precision. In…
Localising Shortcut Learning in Pixel Space via Ordinal Scoring Correlations for Attribution Representations (OSCAR)
Akshit Achara, Peter Triantafillou, Esther Puyol-Antón +2
Deep neural networks often exploit shortcuts. These are spurious cues which are associated with output labels in the training data but are unrelated to task semantics. When the sho…
The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models
Partha Shah, Durva Sankhe, Maariyah Rashid +6
Artificial intelligence (AI) models to automatically classify skin lesions from dermatology images have shown promising performance but also susceptibility to bias by skin tone. Th…