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A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI

arXiv 2211.12565 ↗First posted 22 Nov 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) with PMG and controls from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG MRIs and control MRIs are subtle and the true distribution of the features of the disease is unknown. This makes automatic detection of cases of potential PMG in MRI difficult. We propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM) which enables the automatic detection of cases of potential PMG. Additionally, based on our proposed loss function, we customize a deep learning model structure that integrates dilated convolution, squeeze-and-excitation blocks and feature fusion for our PPMR dataset. Despite working with a small and imbalanced dataset our method achieves 92.01% recall at 55.04% precision. This will facilitate a computer aided tool for radiologists to select potential PMG MRIs. To the best of our knowledge, this research is the first to apply machine learning techniques to identify PMG from MRI only.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 69 references that are in the map. First posted on arXiv 22 Nov 2022.

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References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2017Squeeze-and-Excitation Networkscited by 4,763
  5. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  6. 2020Supervised Contrastive Learningcited by 2,803
  7. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  8. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
  9. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  10. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  11. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  12. 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
12 more
  1. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  2. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  3. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 76
  4. 2017Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classificationcited by 47
  5. 2021Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Imagescited by 45
  6. 2021Anomaly Detection in Medical Imaging -- A Mini Reviewcited by 44
  7. 2020Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Imagescited by 43
  8. 2021COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Predictioncited by 36
  9. 2022ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classificationcited by 25
  10. 2021Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Imagescited by 23
  11. 2020Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networkscited by 13
  12. 2021Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learningcited by 11

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