Promoting Generalization in Cross-Dataset Remote Photoplethysmography
- URL: http://arxiv.org/abs/2305.15199v1
- Date: Wed, 24 May 2023 14:35:54 GMT
- Title: Promoting Generalization in Cross-Dataset Remote Photoplethysmography
- Authors: Nathan Vance, Jeremy Speth, Benjamin Sporrer, Patrick Flynn
- Abstract summary: Remote Photoplethysmography, or the remote monitoring of a subject's heart rate using a camera, has seen a shift from handcrafted techniques to deep learning models.
We show that these models tend to learn a bias to pulse wave features inherent to the training dataset.
We develop augmentations to this learned bias by expanding both the range and variability of heart rates that the model sees while training, resulting in improved model convergence.
- Score: 1.422288795020666
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Remote Photoplethysmography (rPPG), or the remote monitoring of a subject's
heart rate using a camera, has seen a shift from handcrafted techniques to deep
learning models. While current solutions offer substantial performance gains,
we show that these models tend to learn a bias to pulse wave features inherent
to the training dataset. We develop augmentations to mitigate this learned bias
by expanding both the range and variability of heart rates that the model sees
while training, resulting in improved model convergence when training and
cross-dataset generalization at test time. Through a 3-way cross dataset
analysis we demonstrate a reduction in mean absolute error from over 13 beats
per minute to below 3 beats per minute. We compare our method with other recent
rPPG systems, finding similar performance under a variety of evaluation
parameters.
Related papers
- Improved Distribution Matching Distillation for Fast Image Synthesis [54.72356560597428]
We introduce DMD2, a set of techniques that lift this limitation and improve DMD training.
First, we eliminate the regression loss and the need for expensive dataset construction.
Second, we integrate a GAN loss into the distillation procedure, discriminating between generated samples and real images.
arXiv Detail & Related papers (2024-05-23T17:59:49Z) - ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations.
We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
arXiv Detail & Related papers (2024-03-21T17:52:08Z) - The Way to my Heart is through Contrastive Learning: Remote
Photoplethysmography from Unlabelled Video [10.479541955106328]
The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring.
We propose a new approach to remote photoplethysmography (r) - the measurement of blood volume changes from observations of a person's face or skin.
arXiv Detail & Related papers (2021-11-18T15:21:33Z) - Intermediate Layers Matter in Momentum Contrastive Self Supervised
Learning [1.933681537640272]
We show that bringing intermediate layers' representations of two augmented versions of an image closer together in self-supervised learning helps to improve the momentum contrastive (MoCo) method.
We analyze the models trained using our novel approach via feature similarity analysis and layer-wise probing.
arXiv Detail & Related papers (2021-10-27T22:40:41Z) - A Multi-Task Cross-Task Learning Architecture for Ad-hoc Uncertainty
Estimation in 3D Cardiac MRI Image Segmentation [0.0]
We present a Multi-task Cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks.
Our study further showcases the potential of our model to flag low-quality segmentation from a given model.
arXiv Detail & Related papers (2021-09-16T03:53:24Z) - Self-Supervised Learning Framework for Remote Heart Rate Estimation
Using Spatiotemporal Augmentation [12.783744603679942]
Recent deep learning methods have shown that heart rate can be measured remotely using facial videos.
We present a 3D self-supervised learning framework for remote HR estimation on facial videos.
arXiv Detail & Related papers (2021-07-16T04:00:13Z) - On the Robustness of Pretraining and Self-Supervision for a Deep
Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
We compare the impact of different training procedures for diabetic retinopathy grading.
We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions.
Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
arXiv Detail & Related papers (2021-06-25T08:32:45Z) - A Multi-Stage Attentive Transfer Learning Framework for Improving
COVID-19 Diagnosis [49.3704402041314]
We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis.
Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains.
Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
arXiv Detail & Related papers (2021-01-14T01:39:19Z) - Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations [71.00754846434744]
We show that imperceptible additive perturbations can significantly alter the disparity map.
We show that, when used for adversarial data augmentation, our perturbations result in trained models that are more robust.
arXiv Detail & Related papers (2020-09-21T19:20:09Z) - Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on
3D Pelvic CT Images [9.33145393480254]
Training effective deep learning models usually require a large amount of high-quality labeled data.
We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation.
arXiv Detail & Related papers (2020-09-21T01:57:23Z) - Lipreading using Temporal Convolutional Networks [57.41253104365274]
Current model for recognition of isolated words in-the-wild consists of a residual network and Bi-directional Gated Recurrent Unit layers.
We address the limitations of this model and we propose changes which further improve its performance.
Our proposed model results in an absolute improvement of 1.2% and 3.2%, respectively, in these datasets.
arXiv Detail & Related papers (2020-01-23T17:49:35Z)
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.