Lung Nodule Detection

Latest papers

10 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data
Oct 15, 2026Po-Cheng Cheng, Hung-Hsuan ChenLung Nodule Detection Medical Image Representation Learning
Computer Science and Information Engineering, National Central University
NoduleFlow: Automatic 3D Lung Nodule Detection and Segmentation
Oct 12, 2026Giulia Romoli, Filippo Ruffini, Francesco Di Feola +3Lung Nodule Detection Medical Image Segmentation
Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Universitetstorget 4, Umeå, Sweden. · Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, Rome, Italy. · Department of Naval, Electrical, Electronic, and Telecommunications Engineering, University of Genoa, Via Opera Pia 11A, Genoa, Italy.
Uncertainty-driven training for three-dimensional calibrated lung nodule classification
Sep 17, 2026Giuseppe Tripodi, Alessandro De Rosis, Saleh RezaeiraveshLung Nodule Detection Medical Image Classification
School of Engineering, The University of Manchester, Manchester (UK)
Performance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening
Sep 13, 2026Benjamin Renoust, Pierre Baudot, Tiffany Foriel +9Lung Nodule Detection Medical Imaging Foundation Models
Median Technologies, eyonis®, Valbonne, France
GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT
Sep 10, 2026Haojie Yang, Ran SuSmall Object Detection Medical Image Anomaly Detection
College of Intelligence and Computing, Tianjin University, Tianjin, China
NoduLoCC2026: Lung Nodule Localization and Classification Contest from Chest X-Ray Images
Jun 19, 2026Adnan Mustafic, Halim Benhabiles, Adnane Cabani +19Chest X-Ray Classification Medical Image Anomaly Detection
Universit´e de Haute-Alsace, IRIMAS, 68100 Mulhouse, France
Acquisition state behaves as a structured, measurable variable governing lung-nodule AI: kernel-driven measurement instability and noise-driven detection fragility, invisible to DICOM metadata
Jun 11, 2026Daniel SolimanMedical Imaging Medical Image Analysis
Reconstruction Interval Z-Phase Dependence of AI Detection Sensitivity in CT Lung Nodule Screening
May 1, 2026Dan SolimanMedical Image Anomaly Detection Lung Nodule Detection
GammaMetric Medical Physics, Independent Medical Physics Consultancy
Beyond Benchmarks: A Framework for Post Deployment Validation of CT Lung Nodule Detection AI
Mar 25, 2026Daniel SolimanLung Nodule Detection Medical Image Analysis
A Diffusion-Driven Fine-Grained Nodule Synthesis Framework for Enhanced Lung Nodule Detection from Chest Radiographs
Mar 2, 2026Aryan Goyal, Shreshtha Singh, Ashish Mittal +3Lung Nodule Detection Synthetic Data Augmentation
Qure.ai, India · Indian Institute of Technology, Bombay

Oct 15, 2026J/K move · Enter open · S save

L2GSSL: Label-Efficient Local-to-Global Self-Supervised Learning for Volumetric Healthcare Data

Po-Cheng Cheng, Hung-Hsuan Chen

Computer Science and Information Engineering, National Central University

Lung Nodule Detection Medical Image Representation Learning Self-Supervised Pre-Training Data-Efficient Learning Medical Image Classification

Open paper AskPDF arXiv

Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts. We present L2GSSL, a local-to-global self-supervised learning framework that leverages unlabeled volumes by decomposing pretraining into intra-slice and inter-slice representation spaces. A slice encoder learns view-invariant local features, and a global encoder aggregates the resulting tokens to model volumetric context. The stop-gradient isolates the local space from the global objective, while slice-wise execution and a three-phase schedule support training under constrained GPU memory. On LIDC-IDRI lung nodule malignancy classification, L2GSSL reaches

0.9229

AUROC with only

10\%

of the labels and

0.9377

with

25\%

, exceeding the volume-level VICReg by

0.0229

and

0.0294

, respectively. With all labels, the strongest supervised baseline exceeds L2GSSL by only

0.0044

AUROC, consistent with self-supervised pretraining providing its greatest value when annotations are limited. Without adaptation to the external LNDb cohort, a model fine-tuned on

25\%

of LIDC-IDRI obtains

0.8138

AUROC and

0.7589

AUPRC, improving over the strongest evaluated self-supervised baseline by

0.1130

and

0.0954

. Experiments across data sources, thoracic and abdominal anatomy, downstream tasks, and two backbones examine healthcare-data variety, while lesion-slice masking shows that the predictions depend on lesion-bearing content.