arXiv Machine Learning
Jun 8
An Adaptive Data cleaning Framework for Noisy Label Detection
arXiv:2606. 07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets.
The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations
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.
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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.
arXiv Machine Learning
Jun 19
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
arXiv:2606. 20108v1 Announce Type: cross Abstract: Image quality control is vital for a wide range of downstream applications.
Hugging Face Trending Papers
Jun 24
Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation
Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the scarcity of pixel-level annotations. To address this issue, we propose DACL, a semi-supervised framework for robust fetal US image segmentation.
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.
arXiv AI
Aug 11
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
arXiv:2608. 09182v1 Announce Type: cross Abstract: Accurate landmark localization in medical images is a fundamental step for quantitative clinical measurement and downstream analysis.
arXiv Machine Learning
Jun 16
Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation
arXiv:2606. 15837v1 Announce Type: cross Abstract: Deep neural networks (DNNs) frequently fail to generalize to out-of-distribution (OOD) medical images because of variations in scanners and acquisition protocols.
arXiv Computer Vision
Sep 25
When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation
The paper examines how residual misalignments from registration procedures introduce structured label noise in supervised synthetic CT (sCT) generation. It shows that voxel‑wise metrics are heavily influenced by the consistency between training and evaluation registrations, and that training with anatomically consistent registrations reduces variability and improves robustness. Introducing a perceptual loss based on a pretrained Segment Anything encoder yields sharper, more anatomically coherent sCT and highlights the need for anatomy‑oriented evaluation.
arXiv Machine Learning
Jun 5
Towards Label-Noise Resistant Learning via Optimal Brain Damage Masking
arXiv:2508. 09697v3 Announce Type: replace Abstract: Noisy labels are inevitable in real-world scenarios.
arXiv Machine Learning
Jul 23
Label-Noise Resistant Learning via Optimal Brain Damage Masking
arXiv:2508. 09697v4 Announce Type: replace Abstract: Noisy labels are inevitable in real-world multimedia applications.
arXiv Machine Learning
Jul 13
Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification
arXiv:2607. 09402v1 Announce Type: new Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition.
arXiv AI
Jul 2
Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound
arXiv:2607. 00744v1 Announce Type: cross Abstract: Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management.