← Back to all news
arXiv AI By Emanuele Cardinale, Marco Proietti, Alessandro Cacciatore, Maria Francesca Spadea, Lucia Migliorelli, Sara Moccia

The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations

Read the original on arXiv AI →

The paper introduces REMIND, a clustering‑based keypoint‑selection method that uses training dynamics to detect and filter noisy annotations in 2D pose estimation for preterm infants. Applied to the NeoPose dataset of 46 clinical videos, REMIND achieves up to 93% AUC across three pose‑estimation architectures, demonstrating robust learning without prior noise assumptions. This work is the first to explicitly tackle label noise in neonatal pose estimation, enabling more reliable monitoring of infant motor development in real clinical settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
6d ago

Uncertainty-Aware Federated Learning for Infant Movement Analysis

The paper introduces the first federated learning framework for infant movement analysis, specifically targeting General Movement Assessment using skeletal motion data. It employs Monte Carlo Dropout to estimate predictive uncertainty and proposes an Uncertainty-Aware Federated Averaging (UA‑FedAvg) strategy that weights client updates by this uncertainty. Experiments with three clients show that federated learning outperforms local models and approaches centralized training performance, with UA‑FedAvg generally surpassing standard FedAvg.

By Edmond S. L. Ho
safety
More like this →
arXiv AI
Jun 16

Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection

arXiv:2606. 16868v1 Announce Type: cross Abstract: While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels.

By Markus Bujotzek, Dimitrios Bounias, Stefan Denner, Ralf Floca, Maximilian Fischer, Peter Neher, Klaus Maier-Hein
computer-visionbenchmarks
More like this →