Benefit from public unlabeled data: A Frangi filtering-based pretraining network for 3D cerebrovascular segmentation

• URL: http://arxiv.org/abs/2312.15273v1
• Date: Sat, 23 Dec 2023 14:47:21 GMT
• Title: Benefit from public unlabeled data: A Frangi filtering-based pretraining network for 3D cerebrovascular segmentation
• Authors: Gen Shi and Hao Lu and Hui Hui and Jie Tian
• Abstract summary: We construct the largest preprocessed unlabeled TOF-MRA datasets to date. We propose a simple yet effective pertraining strategy based on Frangi filtering. The results have demonstrated the superior performance of our model, with an improvement of approximately 3%.
• Score: 8.611575147737147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The precise cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) data is crucial for clinically computer-aided diagnosis. However, the sparse distribution of cerebrovascular structures in TOF-MRA results in an exceedingly high cost for manual data labeling. The use of unlabeled TOF-MRA data holds the potential to enhance model performance significantly. In this study, we construct the largest preprocessed unlabeled TOF-MRA datasets (1510 subjects) to date. We also provide three additional labeled datasets totaling 113 subjects. Furthermore, we propose a simple yet effective pertraining strategy based on Frangi filtering, known for enhancing vessel-like structures, to fully leverage the unlabeled data for 3D cerebrovascular segmentation. Specifically, we develop a Frangi filtering-based preprocessing workflow to handle the large-scale unlabeled dataset, and a multi-task pretraining strategy is proposed to effectively utilize the preprocessed data. By employing this approach, we maximize the knowledge gained from the unlabeled data. The pretrained model is evaluated on four cerebrovascular segmentation datasets. The results have demonstrated the superior performance of our model, with an improvement of approximately 3\% compared to state-of-the-art semi- and self-supervised methods. Furthermore, the ablation studies also demonstrate the generalizability and effectiveness of the pretraining method regarding the backbone structures. The code and data have been open source at: \url{https://github.com/shigen-StoneRoot/FFPN}.

Related papers

• Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors [0.0]
  Few-shot learning approaches are cost-effective to transfer pre-trained models using only limited annotated data. We train a data-driven sparsification module to enhance airways efficiently in lung CT scans. We then incorporate these sparse representations in a standard supervised segmentation pipeline as a pretraining step to enhance the performance of the DL models.
  arXiv  Detail & Related papers  (2024-07-05T13:46:11Z)
• Extracting Training Data from Unconditional Diffusion Models [76.85077961718875]
  diffusion probabilistic models (DPMs) are being employed as mainstream models for generative artificial intelligence (AI) We aim to establish a theoretical understanding of memorization in DPMs with 1) a memorization metric for theoretical analysis, 2) an analysis of conditional memorization with informative and random labels, and 3) two better evaluation metrics for measuring memorization. Based on the theoretical analysis, we propose a novel data extraction method called textbfSurrogate condItional Data Extraction (SIDE) that leverages a trained on generated data as a surrogate condition to extract training data directly from unconditional diffusion models.
  arXiv  Detail & Related papers  (2024-06-18T16:20:12Z)
• Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation [9.93871075239635]
  We propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods.
  arXiv  Detail & Related papers  (2023-11-17T06:36:43Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Synthetic Augmentation with Large-scale Unconditional Pre-training [4.162192894410251]
  We propose a synthetic augmentation method called HistoDiffusion to reduce the dependency on annotated data. HistoDiffusion can be pre-trained on large-scale unlabeled datasets and later applied to a small-scale labeled dataset for augmented training. We evaluate our proposed method by pre-training on three histopathology datasets and testing on a histopathology dataset of colorectal cancer (CRC) excluded from the pre-training datasets.
  arXiv  Detail & Related papers  (2023-08-08T03:34:04Z)
• A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty [8.604089365903029]
  Total hip anatomy (THA) is a widely used surgical procedure in orthopedics. Deep learning technologies are promising but require high-quality labeled data for the learning. We propose an efficient data annotation pipeline for producing a deep learning-oriented dataset.
  arXiv  Detail & Related papers  (2023-06-07T16:28:53Z)
• Efficient Medical Image Assessment via Self-supervised Learning [27.969767956918503]
  High-performance deep learning methods typically rely on large annotated training datasets. We propose a novel and efficient data assessment strategy to rank the quality of unlabeled medical image data. Motivated by theoretical implication of SSL embedding space, we leverage a Masked Autoencoder for feature extraction.
  arXiv  Detail & Related papers  (2022-09-28T21:39:00Z)
• Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher [54.50747989860957]
  We propose a semi-supervised learning algorithm named Progressive Teacher (PT) to utilize reliable FER datasets as well as large-scale unlabeled expression images for effective training. Experiments on widely-used databases RAF-DB and FERPlus validate the effectiveness of our method, which achieves state-of-the-art performance with accuracy of 89.57% on RAF-DB.
  arXiv  Detail & Related papers  (2022-05-28T07:47:53Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data [53.122045119395594]
  We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. We evaluate a method to identify falsely labeled samples directly during training based on the reconstruction error of the AE.
  arXiv  Detail & Related papers  (2022-04-12T13:05:18Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.