arXiv:2607. 13738v2 Announce Type: replace-cross Abstract: Deep video models estimate left-ventricular ejection fraction (EF) from echocardiography with near-expert accuracy, and post-hoc attribution is increasingly used to certify that such models look at the right place.
By Hyunkyung Han, Min Jung Kim
arXiv:2607. 13738v1 Announce Type: cross Abstract: Background and Objective: Deep video models estimate left-ventricular ejection fraction (EF) from echocardiography with near-expert accuracy, and post-hoc attribution (Chefer relevance for transformers, Grad-CAM for CNNs) is increasingly used to certify that models "look at the right place.
By Hyunkyung Han, Min Jung Kim
arXiv:2607. 25748v1 Announce Type: new Abstract: Objective: Concept bottleneck models route prediction through interpretable intermediate variables, and their validity is normally judged by how accurately those variables are predicted.
By Hyunkyung Han, Min Jung Kim
The paper introduces the "segmentation ceiling," a quantitative criterion that determines when explicit left‑ventricular (LV) segmentation can improve ejection fraction (EF) regression. By deriving how per‑frame segmentation area error propagates into EF error, the authors find that a break‑even error of about 10% per frame is required, whereas typical segmenters exceed this threshold (~14%). Consequently, several strategies that incorporate segmentation or area information fail to outperform a raw‑video baseline, while techniques such as weight averaging with strong augmentation and a heteroscedastic beta‑NLL loss yield competitive EF predictions and well‑calibrated uncertainty estimates.
By Farshid Farhadi Khouzani, Paul La Plante, Bryar Mustafa Shareef, Laxmi Gewali
The paper introduces the first publicly available dataset of over 25,000 parasternal long‑axis (PLAX) echocardiography videos labeled for left ventricular ejection fraction (EF), created through a novel data‑generation strategy that links clinical notes to video data. Using this dataset, the authors train a reproducible PLAX‑based EF model that achieves a mean absolute error (MAE) of 6.86%, comparable to the clinical standard of apical four‑chamber (A4C) methods. They further show that simple late fusion of PLAX and A4C predictions reduces MAE to 6.37%, highlighting the benefit of multi‑view integration, and release the dataset, models, and demos publicly.
By Zhiyuan Gao, Dominic Yurk, Yaser S. Abu-Mostafa
The paper introduces RouteCert, a method for ensuring risk control in multimodal systems that acquire inputs adaptively. It shows that conditional calibration can remain valid even when the acquisition policy determines the calibration group, and provides two finite‑sample constructions: threshold‑free routing with terminal‑pattern calibration and simultaneous validation of policy‑pattern pairs. Experiments on a clinical ECG task and masked multimodal benchmarks demonstrate that RouteCert achieves low disagreement rates and competitive answered fractions while validating each acquisition stage separately.
By Melika Baghi