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

• URL: http://arxiv.org/abs/2512.03339v1
• Date: Wed, 03 Dec 2025 01:11:28 GMT
• Title: ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography
• Authors: Yeganeh Ghamary, Victoria Wu, Hooman Vaseli, Christina Luong, Teresa Tsang, Siavash Bigdeli, Purang Abolmaesumi,
• Abstract summary: ProtoEFNet is a video-based prototype learning model for continuous EF regression.<n>It learns dynamictemporal prototypes that capture clinically meaningful cardiac motion patterns.<n>Our experiments show that ProtoEFNet can achieve accuracy on par with its non-interpretable counterpart.
• Score: 3.041654619898153
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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$\pm$2.68 to 79.64$\pm$2.10. Our source code is available at: https://github.com/DeepRCL/ProtoEF

Related papers

• Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
  We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
  arXiv  Detail & Related papers  (2026-02-27T04:49:01Z)
• Learning a Generative Meta-Model of LLM Activations [75.30161960337892]
  We create "meta-models" that learn the distribution of a network's internal states.<n>Applying the meta-model's learned prior to steering interventions improves fluency, with larger gains as loss decreases.<n>These results suggest generative meta-models offer a scalable path toward interpretability without restrictive structural assumptions.
  arXiv  Detail & Related papers  (2026-02-06T18:59:56Z)
• UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model [53.34835793648352]
  We propose UniSegDiff, a novel diffusion model framework for lesion segmentation.<n>UniSegDiff addresses lesion segmentation in a unified manner across multiple modalities and organs.<n> Comprehensive experimental results demonstrate that UniSegDiff significantly outperforms previous state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2025-07-24T12:33:10Z)
• Echo-E$^3$Net: Efficient Endo-Epi Spatio-Temporal Network for Ejection Fraction Estimation [6.230277002017236]
  Left ventricular ejection fraction (LVEF) is a critical metric for assessing cardiac function, widely used in diagnosing heart failure and guiding clinical decisions.<n>Recent deep learning advancements have enhanced automation, yet many existing models are computationally demanding.<n>We propose Echo-E$3$Net, an efficient Endo-Epi-temporal network tailored for LVEF estimation.
  arXiv  Detail & Related papers  (2025-03-21T21:24:44Z)
• Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data [45.75724873443564]
  Domain shift poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy grading.<n>We propose a novel approach, Generative Unadversarial ExampleS (GUES), which enables adaptation from a data-centric perspective.
  arXiv  Detail & Related papers  (2024-12-02T07:14:25Z)
• EchoNarrator: Generating natural text explanations for ejection fraction predictions [1.3082208571657106]
  Ejection fraction (EF) of the left ventricle (LV) is considered as one of the most important measurements for diagnosing acute heart failure. Recent successes in deep learning research successfully estimate EF values, but the proposed models often lack an explanation for the prediction. We propose a model that combines estimation of the LV contour over multiple frames, together with a set of modules and routines for computing various motion and shape attributes. It then feeds the attributes into a large language model to generate text that helps to explain the network's outcome in a human-like manner.
  arXiv  Detail & Related papers  (2024-10-31T08:59:34Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis [42.810247034149214]
  We propose CoReEcho, a novel training framework emphasizing continuous representations tailored for direct EF regression. CoReEcho: 1) outperforms the current state-of-the-art (SOTA) on the largest echocardiography dataset (EchoNet-Dynamic) with MAE of 3.90 & R2 of 82.44, and 2) provides robust and generalizable features that transfer more effectively in related downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T10:18:06Z)
• ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography [4.908455453888849]
  Aortic stenosis (AS) is a common heart valve valve disease that requires accurate and timely diagnosis for appropriate treatment. Most current automatic detection methods rely on black-box models with a low level of trustworthiness, which hinders clinical adoption. We propose ProtoAS, prototypical a network that detects directly AS from Bmode echocardiography videos. ProtoASNet provides interpretability and an uncertainty measure for each prediction, which can improve transparency and facilitate the interactive usage of deep networks to aid clinical decision-making.
  arXiv  Detail & Related papers  (2023-07-26T18:06:25Z)
• EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks [26.817931695658583]
  Ejection fraction (EF) is a key indicator of cardiac function, allowing identification of patients prone to heart dysfunctions such as heart failure. EF is estimated from cardiac ultrasound videos known as echocardiograms (echo) by manually tracing the left ventricle and estimating its volume on certain frames. These estimations exhibit high inter-observer variability due to the manual process and varying video quality. We introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos.
  arXiv  Detail & Related papers  (2022-08-30T05:59:57Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• Differentiable Deconvolution for Improved Stroke Perfusion Analysis [0.0]
  arterial input function (AIF) is still controversial how and where it should be chosen. We propose an AIF selection approach that is optimized for maximal core lesion segmentation performance. We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
  arXiv  Detail & Related papers  (2021-03-31T14:29:36Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.