Template Collapse and Information-Theoretic Limits in Camera rPPG Pulse Morphology Restoration
- URL: http://arxiv.org/abs/2606.03802v1
- Date: Tue, 02 Jun 2026 15:50:07 GMT
- Title: Template Collapse and Information-Theoretic Limits in Camera rPPG Pulse Morphology Restoration
- Abstract summary: Consumer face camera remote photoplethys hallucination (r) enables passive cardiovascular monitoring.<n>But whether single-cycle waveform morphology encoding arterial stiffness is recoverable from this measurement has not been characterised.<n>We evaluated 16 encoder architectures spanning six families on 153 subjects across three datasets.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Objective: Consumer face camera remote photoplethysmography (rPPG) enables passive cardiovascular monitoring, but whether single-cycle waveform morphology encoding arterial stiffness biomarkers is recoverable from this measurement has not been characterised. Methods: We evaluated 16 architectures spanning six families on 153 subjects across three datasets, introducing cross-subject Pearson r to distinguish subject-specific recovery from template collapse. Results: No architecture recovered subject-specific morphology (cross-subject r range 0.773--0.9999; ground-truth ceiling 0.601). Supervised Contrastive (SupCon) converged to log N = 4.844, constituting the strongest available empirical evidence that no discriminative morphological structure is extractable from single-cycle rPPG by the encoder families tested. The VAE decoder restores population-level harmonic content absent from the rPPG input (H2/H1: 0.310 output vs. 0.275 input), generalising zero-shot to UBFC (r = +0.708); a directional hallucination gap (p = 0.150) suggests partial signal reading. Anti-collapse objectives fail when input carries no discriminative structure. Significance: Consumer cameras cannot encode individual arterial morphology; cross-subject r is a necessary collapse diagnostic for waveform reconstruction benchmarks.
Related papers
- B-MIM: Biased Masked Image Modeling for Generalizable Segmentation of Fine-Grained Anatomical Structures [42.844780603412325]
Self-supervised pretraining enables transferable representations for medical imaging.<n>Most CT encoders remain biased toward coarse semantic understanding.<n>We introduce Biased Masked Image Modeling to reduce global semantic alignment.
arXiv Detail & Related papers (2026-08-25T10:20:05Z) - Beyond Static Anchors: Bounded Prototype Conditioning for Language-Free Medical Anomaly Detection [52.986874008247554]
We propose ReCAP, a language-free framework that replaces static anchors with input-conditioned visual prototypes.<n>ReCAP re-centers separated normal and abnormal prototypes for each image through a bounded gated modulation.<n>It achieves the best image-level AUROC on all zero-shot and 23 of 24 few-shot settings, and the best zero-shot pixel-level AUROC on all three segmentation datasets.
arXiv Detail & Related papers (2026-08-01T04:46:55Z) - Physiological Signals as a Forensic Modality for Talking-Face Deepfake Detection [0.0]
Talking-face (TF) deep-swap generation synthesizes photore- alistic facial video from a static source image and an au-fake signal, producing forgeries that current image-based detectors consistently fail to identify.<n>We propose a detection framework that extracts per-video r wave forms via RhythmFormer and trains a suite of lightweight classifiers to distinguish real signals.
arXiv Detail & Related papers (2026-07-23T19:47:15Z) - BAC-JEPA: Label-Efficient Breast Arterial Calcification Segmentation via Synthetic Mammography-Guided Supervision [0.0]
We present BAC-JEPA, a label-efficient segmentation framework trained on procedurally generated arterial calcification inserted into real mammographic backgrounds with exact masks.<n>Synthetic masks are paired with mammography self-supervised Vision Transformer encoders and a high-resolution convolutional decoder to produce full-resolution segmentation maps.<n>On BacSeg, image-level classification from segmentation probability maps reached AUROC 0.8719, with 0.8547 for the smaller backbone.
arXiv Detail & Related papers (2026-06-20T15:15:11Z) - Phenotyping TPF via Self-Supervised Learning: A Label-Agnostic Framework with Expert Validation [2.201241345037463]
We design, implement, and validate a label-agnostic framework to learn fracture representations directly from imaging data without observer-assigned labels.<n>A RadImageNet-pretrained ResNet-50 encoder is fine-tuned on 154 cleaned knee radiographs.<n>We show that label-agnostic SSL phenotyping is a reproducible and clinically interpretable complement to conventional classification.
arXiv Detail & Related papers (2026-06-15T21:04:31Z) - Anatomy Guided Coronary Artery Segmentation from CCTA Using Spatial Frequency Joint Modeling [10.73944733412427]
We propose a coronary artery segmentation framework that integrates myocardial anatomical priors, structure aware feature encoding, and three dimensional wavelet inverse wavelet transformations.<n> Experimental results demonstrate that the proposed method achieves a Dice coefficient of 0.8082, Sensitivity of 0.7946, Precision of 0.8471, and an HD95 of 9.77 mm, outperforming several mainstream segmentation models.
arXiv Detail & Related papers (2025-12-14T04:12:40Z) - Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT [68.09387763135236]
We introduce a weakly supervised method trained solely on vertebra-level healthy/malignant labels, without any lesion masks.<n>We achieve strong blastic/lytic performance despite no mask supervision.
arXiv Detail & Related papers (2025-12-07T14:03:28Z) - I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging [2.384534878752428]
We introduce an unsupervised, oracle-free framework that incrementally expands a trusted set of normal samples without any anomaly labels.<n>A frozen pretrained vision backbone is augmented with tiny convolutional adapters, ensuring rapid domain adaptation with negligible computational overhead.<n>On COVID-CXR, ROC-AUC improves from 0.9489 to 0.9982; on Pneumonia CXR, ROC-AUC rises from 0.6834 to 0.8968; and on Brain MRI ND-5, ROC-AUC increases from 0.6041 to 0.7269.
arXiv Detail & Related papers (2025-11-05T23:28:14Z) - KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module.
Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules.
The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
arXiv Detail & Related papers (2024-10-28T16:00:42Z) - Deep Learning for Vascular Segmentation and Applications in Phase
Contrast Tomography Imaging [33.23991248643144]
We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs.
Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality.
HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel.
arXiv Detail & Related papers (2023-11-22T11:15:38Z) - A Two-Stage Generative Model with CycleGAN and Joint Diffusion for
MRI-based Brain Tumor Detection [41.454028276986946]
We propose a novel framework Two-Stage Generative Model (TSGM) to improve brain tumor detection and segmentation.
CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior.
VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide.
arXiv Detail & Related papers (2023-11-06T12:58:26Z) - WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic
Segmentation for Lung Adenocarcinoma [51.50991881342181]
This challenge includes 10,091 patch-level annotations and over 130 million labeled pixels.
First place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919)
arXiv Detail & Related papers (2022-04-13T15:27:05Z) - Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks.
Our results demonstrate that most popular standardization steps add no value to the network performance.
We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
arXiv Detail & Related papers (2022-04-11T17:29:36Z) - Feature-enhanced Adversarial Semi-supervised Semantic Segmentation
Network for Pulmonary Embolism Annotation [6.142272540492936]
This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas.
In current studies, all of the PEA image segmentation methods are trained by supervised learning.
This study proposed a semi-supervised learning method to make the model applicable to different datasets by adding a small amount of unlabeled images.
arXiv Detail & Related papers (2022-04-08T04:21:02Z) - Vision Transformers for femur fracture classification [59.99241204074268]
The Vision Transformer (ViT) was able to correctly predict 83% of the test images.
Good results were obtained in sub-fractures with the largest and richest dataset ever.
arXiv Detail & Related papers (2021-08-07T10:12:42Z)
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.