# ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography

arXiv: https://arxiv.org/abs/2512.03339 · HTML: https://mlsanity.com/paper/2512.03339/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations](https://mlsanity.com/paper/2103.10663/) (2021)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)
- [EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks](https://mlsanity.com/paper/2208.14003/) (2022)
- [EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography](https://mlsanity.com/paper/2209.04242/) (2022)
- [ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography](https://mlsanity.com/paper/2307.14433/) (2023)
- [GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis](https://mlsanity.com/paper/2308.13217/) (2023)
- [INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples](https://mlsanity.com/paper/2208.00457/) (2022)
- [CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis](https://mlsanity.com/paper/2403.10164/) (2024)

## Built on by (most cited first, 2 of 2)

- [Ablation-Corrected Evaluation of Attribution Maps in Echocardiographic Ejection-Fraction Models](https://mlsanity.com/paper/2607.13738/) (2026)
- [HypOProto: Hyperbolic Ordinal Prototypes for Left Ventricular Filling Pressure Classification](https://mlsanity.com/paper/2606.19804/) (2026)

## Abstract (arXiv)

Ejection fraction (EF) is a crucial metric for assessing cardiac function and diagnosing conditions such as heart failure. Traditionally, EF estimation requires manual tracing and domain expertise, making the process time-consuming and subject to interobserver variability. Most current deep learning methods for EF prediction are black-box models with limited transparency, which reduces clinical trust. Some post-hoc explainability methods have been proposed to interpret the decision-making process after the prediction is made. However, these explanations do not guide the model's internal reasoning and therefore offer limited reliability in clinical applications. To address this, we introduce ProtoEFNet, a novel video-based prototype learning model for continuous EF regression. The model learns dynamic spatiotemporal prototypes that capture clinically meaningful cardiac motion patterns. Additionally, the proposed Prototype Angular Separation (PAS) loss enforces discriminative representations across the continuous EF spectrum. Our experiments on the EchonetDynamic dataset show that ProtoEFNet can achieve accuracy on par with its non-interpretable counterpart while providing clinically relevant insight. The ablation study shows that the proposed loss boosts performance with a 2% increase in F1 score from 77.67±2.68 to 79.64±2.10. Our source code is available at: https://github.com/DeepRCL/ProtoEF