Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children

• URL: http://arxiv.org/abs/2210.04767v1
• Date: Thu, 6 Oct 2022 14:21:44 GMT
• Title: Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children
• Authors: Henok Ghebrechristos, Stence Nicholas, David Mirsky, Gita Alaghband, Manh Huynh, Zackary Kromer, Ligia Batista, Brent ONeill, Steven Moulton, Daniel M.Lindberg
• Abstract summary: This paper presents a deep learning framework for image classification aimed at increasing predictive performance for Cytotoxic Edema (CE) diagnosis in infants and children. The proposed framework includes two 3D network architectures optimized to learn from two types of clinical MRI data.
• Score: 0.6291681227094761
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents a deep learning framework for image classification aimed at increasing predictive performance for Cytotoxic Edema (CE) diagnosis in infants and children. The proposed framework includes two 3D network architectures optimized to learn from two types of clinical MRI data , a trace Diffusion Weighted Image (DWI) and the calculated Apparent Diffusion Coefficient map (ADC). This work proposes a robust and novel solution based on volumetric analysis of 3D images (using pixels from time slices) and 3D convolutional neural network (CNN) models. While simple in architecture, the proposed framework shows significant quantitative results on the domain problem. We use a dataset curated from a Childrens Hospital Colorado (CHCO) patient registry to report a predictive performance F1 score of 0.91 at distinguishing CE patients from children with severe neurologic injury without CE. In addition, we perform analysis of our systems output to determine the association of CE with Abusive Head Trauma (AHT) , a type of traumatic brain injury (TBI) associated with abuse , and overall functional outcome and in hospital mortality of infants and young children. We used two clinical variables, AHT diagnosis and Functional Status Scale (FSS) score, to arrive at the conclusion that CE is highly correlated with overall outcome and that further study is needed to determine whether CE is a biomarker of AHT. With that, this paper introduces a simple yet powerful deep learning based solution for automated CE classification. This solution also enables an indepth analysis of progression of CE and its correlation to AHT and overall neurologic outcome, which in turn has the potential to empower experts to diagnose and mitigate AHT during early stages of a childs life.

Related papers

• Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models [5.817643726988823]
  This study explores the application of artificial intelligence (AI) and machine learning (ML) to improve the speed and accuracy of abdominal trauma diagnosis. We developed an advanced AI-based model combining 3D segmentation, 2D Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic performance. Our model processes abdominal CT scans to provide real-time, precise assessments, thereby improving clinical decision-making and patient outcomes.
  arXiv  Detail & Related papers  (2024-07-23T04:18:34Z)
• Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy [41.94295877935867]
  This study highlights the importance of preprocessing and segmentation in improving the reliability of ADHD diagnosis through EEG. Models trained on later EEG segments achieved significantly higher accuracy, indicating the potential role of cognitive fatigue in distinguishing ADHD.
  arXiv  Detail & Related papers  (2024-07-11T09:07:22Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning [1.4149937986822438]
  Stroke is a common disabling neurological condition that affects about one-quarter of the adult population over age 25. Our proposed fusion model achieves 0.87, 0.80 and 80.45% for AUC, F1-score and accuracy, respectively.
  arXiv  Detail & Related papers  (2024-02-16T18:51:42Z)
• Successive Subspace Learning for Cardiac Disease Classification with Two-phase Deformation Fields from Cine MRI [36.044984400761535]
  This work proposes a lightweight successive subspace learning framework for CVD classification. It is based on an interpretable feedforward design, in conjunction with a cardiac atlas. Compared with 3D CNN-based approaches, our framework achieves superior classification performance with 140$times$ fewer parameters.
  arXiv  Detail & Related papers  (2023-01-21T15:00:59Z)
• Outlier-based Autism Detection using Longitudinal Structural MRI [6.311381904410801]
  This paper proposes structural Magnetic Resonance Imaging (sMRI)-based Autism Spectrum Disorder diagnosis via an outlier detection approach. Generative Adversarial Network (GAN) is trained exclusively with sMRI scans of healthy subjects. Experiments reveal that our ASD detection framework performs comparably with the state-of-the-art with far fewer training data.
  arXiv  Detail & Related papers  (2022-02-21T04:37:25Z)
• 3D Structural Analysis of the Optic Nerve Head to Robustly Discriminate Between Papilledema and Optic Disc Drusen [44.754910718620295]
  We developed a deep learning algorithm to identify major tissue structures of the optic nerve head (ONH) in 3D optical coherence tomography ( OCT) scans. A classification algorithm was designed using 150 OCT volumes to perform 3-class classifications (1: ODD, 2: papilledema, 3: healthy) strictly from their drusen and prelamina swelling scores. Our AI approach accurately discriminated ODD from papilledema, using a single OCT scan.
  arXiv  Detail & Related papers  (2021-12-18T17:05:53Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation [53.187152856583396]
  We propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features.
  arXiv  Detail & Related papers  (2020-10-09T08:22:54Z)
• Grading the severity of hypoxic-ischemic encephalopathy in newborn EEG using a convolutional neural network [3.2498534294827044]
  This study presents a novel end-to-end architecture, using a deep convolutional neural network, that learns hierarchical representations within raw EEG data. The system classifies 4 grades of hypoxic-ischemic encephalopathy and is evaluated on a multi-channel EEG dataset of 63 hours from 54 newborns.
  arXiv  Detail & Related papers  (2020-05-12T05:58:27Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.