Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI

• URL: http://arxiv.org/abs/2605.09575v1
• Date: Sun, 10 May 2026 14:39:30 GMT
• Title: Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
• Abstract summary: Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment.<n>Deep learning models offer potential for automation but typically require large annotated datasets.<n>FreeHemoSeg was developed and trained using pseudo GMH-IVH images synthesized from normal fetal data.
• Score: 35.75434967398725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment. Manual diagnosis and lesion segmentation are labor-intensive and error-prone. Deep learning models offer potential for automation but typically require large annotated datasets, which are challenging to obtain. Purpose: To develop and validate an annotation-free deep learning framework for automated detection and segmentation of GMH-IVH on brain MRI. Materials and Methods: This retrospective study analyzed 2D T2-weighted MRI data from pregnant women collected from October 2015 to October 2023 at one hospital (internal validation) and two hospitals (external validation). Eligible participants included healthy fetuses and those with GMH-IVH. FreeHemoSeg was developed and trained using pseudo GMH-IVH images synthesized from normal fetal data guided by medical priors. Primary outcomes included diagnostic accuracy (area under the ROC curve [AUROC], sensitivity, specificity) and segmentation accuracy (Dice similarity coefficient [DSC]). A reader study evaluated clinical utility. Results: A total of 1674 stacks from 558 pregnant women were analyzed. FreeHemoSeg achieved the highest performance in both internal (sensitivity: 0.914, 95% CI 0.869-0.945; specificity: 0.966, 95% CI 0.946-0.978; DSC: 0.559, 95% CI 0.546-0.571) and external validation (sensitivity: 0.824, 95% CI 0.739-0.885; specificity: 0.943, 95% CI 0.913-0.964; DSC: 0.512, 95% CI 0.497-0.526), outperforming supervised and unsupervised methods. FreeHemoSeg assistance improved radiologists' sensitivity (from 0.882 to 0.941-1.000) and diagnostic confidence while reducing interpretation time by 16.0-52.7%. Conclusion: FreeHemoSeg accurately detects and localizes fetal brain hemorrhages without annotated training data, enabling earlier diagnosis and supporting timely clinical management.

Related papers

• An Explainable AI-Driven Framework for Automated Brain Tumor Segmentation Using an Attention-Enhanced U-Net [0.6087515951830486]
  We present a new segmentation technique based on a U-Net model augmented with executed attention gates to focus on the most significant regions of images.<n>To counter class imbalance, we employ manually designed loss functions like Dice Loss and Categorical Dice Loss, in conjunction with standard categorical cross-entropy.<n>Our approach achieved superior performance with accuracy of 0.9919, Dice coefficient of 0.9901, mean IoU of 0.9873, sensitivity of 0.9908, and specificity of 0.9974.
  arXiv  Detail & Related papers  (2026-03-24T15:42:49Z)
• FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation [63.7829089874007]
  This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation.<n>FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images.<n> Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2.
  arXiv  Detail & Related papers  (2026-01-22T01:34:39Z)
• Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries [65.4286079244589]
  Prolonged length of stay (pLoS) is a significant factor associated with the risk of adverse in-hospital events.<n>We develop and explain a predictive model for pLos using admission-level patient and hospital administrative data.
  arXiv  Detail & Related papers  (2026-01-07T23:35:24Z)
• An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection [55.35661671061754]
  Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas.<n>We propose a framework which enhances disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head.<n>Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection.
  arXiv  Detail & Related papers  (2025-10-21T17:18:55Z)
• An Explainable AI-Enhanced Machine Learning Approach for Cardiovascular Disease Detection and Risk Assessment [0.0]
  Heart disease remains a major global health concern.<n>Traditional diagnostic methods fail to accurately identify and manage heart disease risks.<n>Machine learning has the potential to significantly enhance the accuracy, efficiency, and speed of heart disease diagnosis.
  arXiv  Detail & Related papers  (2025-07-15T10:38:38Z)
• An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework [4.264452248986976]
  VIPEEGNet was developed and validated using EEGs recorded from Massachusetts General Hospital/Harvard Medical School.<n>For multi classification, the sensitivity of VIPEEGNET for the six categories ranges from 36.8% to 88.2%.
  arXiv  Detail & Related papers  (2025-07-10T02:22:13Z)
• Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV [49.1574468325115]
  This study explores multiple ML approaches for predicting LOS in ICU specifically for the patients with neurological diseases based on the MIMIC-IV dataset.<n>The evaluated models include classic ML algorithms (K-Nearest Neighbors, Random Forest, XGBoost and CatBoost) and Neural Networks (LSTM, BERT and Temporal Fusion Transformer)
  arXiv  Detail & Related papers  (2025-05-23T14:06:42Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan [40.51754649947294]
  The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. The model's diagnostic performance was compared with clinicians's performance. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation.
  arXiv  Detail & Related papers  (2023-02-02T08:45:17Z)
• Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip [3.216812203515066]
  The clinical diagnosis of developmental dysplasia of the hip typically involves manually measuring key radiological angles.<n>This study aims to develop an automated system that integrates these measurements to enhance the accuracy and consistency of DDH diagnosis.
  arXiv  Detail & Related papers  (2022-09-07T19:50:30Z)
• Weak labels and anatomical knowledge: making deep learning practical for intracranial aneurysm detection in TOF-MRA [0.0]
  We develop a fully automated, deep neural network that is trained utilizing oversized weak labels. Our network achieves an average sensitivity of 77% on our in-house data, with a mean False Positive (FP) rate of 0.72 per patient.
  arXiv  Detail & Related papers  (2021-03-10T16:31:54Z)

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.