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
- 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) - PCRLv2: A Unified Visual Information Preservation Framework for
Self-supervised Pre-training in Medical Image Analysis [56.63327669853693]
We propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics.
We also address the preservation of scale information, a powerful tool in aiding image understanding.
The proposed unified SSL framework surpasses its self-supervised counterparts on various tasks.
arXiv Detail & Related papers (2023-01-02T17:47:27Z) - SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality
Classification from MRI [5.199134881541244]
We propose a slice-based self-supervised deep learning framework (SBSSL) for classifying abnormality using knee MRI scans.
For a limited number of cases (1000), our proposed framework is capable to identify anterior cruciate ligament tear with an accuracy of 89.17% and an AUC of 0.954.
arXiv Detail & Related papers (2022-08-29T23:08:41Z) - 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.