FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning

• URL: http://arxiv.org/abs/2403.11032v1
• Date: Sat, 16 Mar 2024 22:35:21 GMT
• Title: FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning
• Abstract summary: Familial Hypercholesterolemia (FH) is a genetic disorder characterized by elevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated genes. Conventional diagnosis approach is complex, costly, and a challenging interpretation task. Deep Learning (DL) for FH detection is in its infancy, possibly, due to categorical nature of the underlying clinical data.
• Score: 4.9278815557931015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Familial Hypercholesterolemia (FH) is a genetic disorder characterized by elevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated genes. Early-stage and accurate categorization of FH is of significance allowing for timely interventions to mitigate the risk of life-threatening conditions. Conventional diagnosis approach, however, is complex, costly, and a challenging interpretation task even for experienced clinicians resulting in high underdiagnosis rates. Although there has been a recent surge of interest in using Machine Learning (ML) models for early FH detection, existing solutions only consider a binary classification task solely using classical ML models. Despite its significance, application of Deep Learning (DL) for FH detection is in its infancy, possibly, due to categorical nature of the underlying clinical data. The paper addresses this gap by introducing the FH-TabNet, which is a multi-stage tabular DL network for multi-class (Definite, Probable, Possible, and Unlikely) FH detection. The FH-TabNet initially involves applying a deep tabular data learning architecture (TabNet) for primary categorization into healthy (Possible/Unlikely) and patient (Probable/Definite) classes. Subsequently, independent TabNet classifiers are applied to each subgroup, enabling refined classification. The model's performance is evaluated through 5-fold cross-validation illustrating superior performance in categorizing FH patients, particularly in the challenging low-prevalence subcategories.

Related papers

• Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data [76.89269238957593]
  Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views.<n>We propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification.
  arXiv  Detail & Related papers  (2026-02-02T13:07:52Z)
• Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings [0.9668407688201359]
  This study investigates the feasibility and performance of federated learning for multi-label ICD code classification.<n>We propose a scalable pipeline combining frozen text embeddings with simple multilayer perceptron (MLP) classifiers.
  arXiv  Detail & Related papers  (2025-07-03T18:58:36Z)
• Anatomy-Guided Multitask Learning for MRI-Based Classification of Placenta Accreta Spectrum and its Subtypes [8.27670531391613]
  Placenta Accreta Spectrum Disorders (PAS) pose significant risks during pregnancy.<n>We propose a novel convolutional neural network (CNN) architecture designed for efficient one-stage multiclass diagnosis of PAS and its subtypes.
  arXiv  Detail & Related papers  (2025-05-23T05:22:30Z)
• ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification [3.6535793744942318]
  We propose the ActiveSSF framework, which integrates active learning with self-supervised pretraining. Experimental results on clinical megakaryocyte datasets demonstrate that ActiveSSF achieves state-of-the-art performance. To foster further research, the code and datasets will be publicly released in the future.
  arXiv  Detail & Related papers  (2025-02-12T08:24:36Z)
• Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction [1.7709249262395883]
  This work explores injecting hierarchical prior knowledge into graph neural networks (GNNs) for single-cell multi-class classification of cellular data. We propose our hierarchical plug-in method to be applied to several GNN models, namely, FCHC-GNN, and effectively designed to capture neighborhood information crucial for single-cell FC domain.
  arXiv  Detail & Related papers  (2024-05-28T18:24:16Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Balanced Classification: A Unified Framework for Long-Tailed Object Detection [74.94216414011326]
  Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution. BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
  arXiv  Detail & Related papers  (2023-08-04T09:11:07Z)
• Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images [7.159532626507458]
  This study introduces a Graph Convolution Network (GCN) to exploit prior co-occurrence between each category as a correlation matrix into the deep learning model for the multi-label classification. We propose a Graph-Ensemble Learning Model (GELN) that views the prediction from GCN as complementary information of the predictions from the fusion model.
  arXiv  Detail & Related papers  (2023-07-04T13:19:57Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• One-Class Risk Estimation for One-Class Hyperspectral Image Classification [8.206701378422968]
  Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI. Deep learning-based methods are currently the mainstream to overcome distribution overlap in HSI multiclassification. In this article, a weakly supervised deep HSI one-class classification, HOneCls, is proposed.
  arXiv  Detail & Related papers  (2022-10-27T14:15:13Z)
• Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data [14.74172876249796]
  COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. It is beneficial to prioritize the identification of more severe classes and control the "under-classification" errors. Here, we propose a hierarchical NP (H-NP) framework and an umbrella algorithm that controls the under-classification errors with high probability.
  arXiv  Detail & Related papers  (2022-10-01T06:11:30Z)
• Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data [7.422597776308963]
  We propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses.
  arXiv  Detail & Related papers  (2022-06-18T03:52:43Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification [76.89093364969253]
  We propose to apply Few-Shot Learning to skin disease identification to address the extreme scarcity of training sample problem. Based on a detailed analysis, we propose the Query-Relative (QR) loss, which proves superior to Cross Entropy (CE) under episode training. We further strengthen the proposed QR loss with a novel adaptive hard margin strategy.
  arXiv  Detail & Related papers  (2020-04-21T00:57:11Z)

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.