# Ablation-Corrected Evaluation of Attribution Maps in Echocardiographic Ejection-Fraction Models

arXiv: https://arxiv.org/abs/2607.13738 · HTML: https://mlsanity.com/paper/2607.13738/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 12 of 12)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography](https://mlsanity.com/paper/2512.03339/) (2025)

## Built on by (most cited first, 1 of 1)

- [Loss Invariance Determines What Concept Layers Encode: Volume Grounding in Echocardiography](https://mlsanity.com/paper/2607.25748/) (2026)

## 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.