Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification

• URL: http://arxiv.org/abs/2301.00409v1
• Date: Sun, 1 Jan 2023 14:19:52 GMT
• Title: Diffusion Model based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification
• Authors: Shizhan Gong, Cheng Chen, Yuqi Gong, Nga Yan Chan, Wenao Ma, Calvin Hoi-Kwan Mak, Jill Abrigo, Qi Dou
• Abstract summary: Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. We propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.
• Score: 14.978566465420572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantification not only require intensive labeling in millimeter-level measurement but also suffer from poor performance due to their dependence on specific landmarks or simplified anatomical assumptions. In this paper, we propose a novel semi-supervised framework to accurately measure the scale of MLS from head CT scans. We formulate the MLS measurement task as a deformation estimation problem and solve it using a few MLS slices with sparse labels. Meanwhile, with the help of diffusion models, we are able to use a great number of unlabeled MLS data and 2793 non-MLS cases for representation learning and regularization. The extracted representation reflects how the image is different from a non-MLS image and regularization serves an important role in the sparse-to-dense refinement of the deformation field. Our experiment on a real clinical brain hemorrhage dataset has achieved state-of-the-art performance and can generate interpretable deformation fields.

Related papers

• Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding [92.32881381717594]
  We introduce ALternate Contrastive Decoding (ALCD) to solve hallucination issues in medical information extraction tasks. ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.
  arXiv  Detail & Related papers  (2024-10-21T07:19:19Z)
• Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models [7.197545510697077]
  Bone marrow lesions (BMLs) are critical indicators of knee osteoarthritis (OA) effective detection of BMLs in MRI is vital for OA diagnosis and treatment. This paper proposes a semi-supervised local anomaly detection method using mask inpainting models for identification of BMLs in high-resolution knee MRI.
  arXiv  Detail & Related papers  (2024-09-27T23:47:47Z)
• Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging [7.912408164613206]
  In clinical practice, segmenting specific lesions can significantly enhance diagnostic accuracy and treatment efficiency. We propose a novel model, Language-guided Scale-aware MedSegmentor (LSMS), which segments target lesions in medical images based on given textual expressions. Our LSMS consistently achieves superior performance with significantly lower computational cost.
  arXiv  Detail & Related papers  (2024-08-30T15:22:13Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• Robust Fiber ODF Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI [7.9283612449524155]
  A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF) measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling. We propose a novel data-driven deep constrained spherical deconvolution method to
  arXiv  Detail & Related papers  (2023-06-05T14:06:40Z)
• DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images [23.354686734545176]
  We present a novel SSL framework, named DrasCLR, for 3D medical imaging. We propose two domain-specific contrastive learning strategies: one aims to capture subtle disease patterns inside a local anatomical region, and the other aims to represent severe disease patterns that span larger regions.
  arXiv  Detail & Related papers  (2023-02-21T01:32:27Z)
• Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances [102.68466217374655]
  Deep neural networks (DNNs) have rapidly become a textitde facto choice for medical image understanding tasks. In this paper, we propose to use pruning to automatically and adaptively identify textithard-to-learn (HTL) training samples. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances.
  arXiv  Detail & Related papers  (2022-12-06T00:32:03Z)
• How can spherical CNNs benefit ML-based diffusion MRI parameter estimation? [2.4417196796959906]
  Spherical convolutional neural networks (S-CNN) offer distinct advantages over conventional fully-connected networks (FCN) Current clinical practice commonly acquires dMRI data consisting of only 6 diffusion weighted images (DWIs)
  arXiv  Detail & Related papers  (2022-07-01T17:49:26Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images [83.7047542725469]
  Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need of external training data. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming the state-of-the-art alternatives, even while learning from a single slide.
  arXiv  Detail & Related papers  (2021-09-22T15:06:06Z)
• FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset [57.30136148318641]
  Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS) This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. We present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
  arXiv  Detail & Related papers  (2021-06-10T17:14:27Z)
• Conditional Training with Bounding Map for Universal Lesion Detection [33.24904644311758]
  Universal Lesion Detection in computed tomography plays an essential role in computer-aided diagnosis. Two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object proposal. We propose a BM-based conditional training for two-stage ULD, which can reduce positive vs. negative anchor imbalance.
  arXiv  Detail & Related papers  (2021-03-23T03:04:13Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)

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.