Ablation-Corrected Evaluation of Attribution Maps in Echocardiographic Ejection-Fraction Models
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Abstract (arXiv)
Attribution maps for echocardiographic ejection-fraction models are evaluated by their overlap with an expert left-ventricular annotation, compared against a chance level that is computed from an area ratio rather than measured. We measure it. Two architectures trained on the same task attain overlap at 3.55 and 4.20 times measured chance, an eighteen percent difference a reader would take as the size of the gap between them. It is not. Replacing the annotated ventricle with a composition-matched surrogate changes one model's prediction six times more than replacing an equal-area control region and the other's twice, a factor of three; on sixty-two percent of cases for the second, neither the ventricle nor the attributed region moves the prediction at all. The reliability of this measurement, from two independent intervention batches, is 0.90 to 0.98. Overlap does not track the difference: scored against an ablation-derived label of which cases the model depends on the ventricle for, it reaches an area under the curve of 0.52 and 0.67. The design of the ablation also determines its answer, since ablating only the two annotated frames leaves the ventricle indistinguishable from a control region while ablating across all frames does not. A pediatric cohort reproduces all of these. We give the measured chance level, the symmetric-ablation protocol, the reliability estimate, and the ablation-prediction score as things to report alongside overlap.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 15 Jul 2026.
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12 of 18 references.
References · 12
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,322
- 2018Sanity Checks for Saliency Mapscited by 1,156
- 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,143
- 2019Attention is not Explanationcited by 929
- 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
- 2019Attention is not not Explanationcited by 636
- 2020Transformer Interpretability Beyond Attention Visualizationcited by 627
- 2018A Benchmark for Interpretability Methods in Deep Neural Networkscited by 476
- 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 244
- 2025ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiographycited by 2
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Citing papers per year: 2026: 1.
1 paper.