GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain

• URL: http://arxiv.org/abs/2503.22397v1
• Date: Fri, 28 Mar 2025 13:06:45 GMT
• Title: GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain
• Authors: Vida Adeli, Soroush Mehraban, Majid Mirmehdi, Alan Whone, Benjamin Filtjens, Amirhossein Dadashzadeh, Alfonso Fasano, Andrea Iaboni Babak Taati,
• Abstract summary: GAITGen is a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels.<n>Experiments on our new PD-GaM dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality.
• Score: 4.335677334039898
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson's Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of bias. To address these gaps, we propose GAITGen, a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels. GAITGen employs a Conditional Residual Vector Quantized Variational Autoencoder to learn disentangled representations of motion dynamics and pathology-specific factors, coupled with Mask and Residual Transformers for conditioned sequence generation. GAITGen generates realistic, diverse gait sequences across severity levels, enriching datasets and enabling large-scale model training in parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality, accurately capturing critical pathology-specific gait features. A clinical user study confirms the realism and clinical relevance of our generated sequences. Moreover, incorporating GAITGen-generated data into downstream tasks improves parkinsonian gait severity estimation, highlighting its potential for advancing clinical gait analysis.

Related papers

• Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer [1.5416321520529301]
  Efficient Knowledge Adaptation (PEKA) is a novel framework that integrates knowledge distillation and structure alignment losses for cross-modal knowledge transfer. We evaluated PEKA for gene expression prediction using multiple spatial transcriptomics datasets.
  arXiv  Detail & Related papers  (2025-04-09T17:24:41Z)
• Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging [3.6940298700319065]
  PATH-X is a framework that integrates Vision Transformers (ViT) and Autoencoders with SHAP (Shapley Additive Explanations) to enhance modelability for patient stratification and risk prediction. A representative image slice is selected from each WSI, and numerical feature embeddings are extracted using Google's pre-trained ViT. Kaplan-Meier survival analysis is applied to evaluate stratification into two and three risk groups.
  arXiv  Detail & Related papers  (2025-04-07T05:48:42Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Generalizable automated ischaemic stroke lesion segmentation with vision transformers [0.7400397057238803]
  Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke.<n>Current U-Net-based models therefore underperform, a problem accentuated by inadequate evaluation metrics.<n>Here, we present a high-performance DWI lesion segmentation tool addressing these challenges.
  arXiv  Detail & Related papers  (2025-02-10T19:00:00Z)
• Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database [0.9055332067000195]
  Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality.<n> predictive models for lung metastasis inHCC remain limited in scope and clinical applicability.<n>We develop and validate an end-to-end machine learning pipeline using data from the Surveillance, Epidemiology, and End Results (SEER) database.
  arXiv  Detail & Related papers  (2025-01-20T20:06:31Z)
• Integrating Large Language Models for Genetic Variant Classification [12.244115429231888]
  Large Language Models (LLMs) have emerged as transformative tools in genetics. This study investigates the integration of state-of-the-art LLMs, including GPN-MSA, ESM1b, and AlphaMissense. Our approach evaluates these integrated models using the well-annotated ProteinGym and ClinVar datasets.
  arXiv  Detail & Related papers  (2024-11-07T13:45:56Z)
• Large-Scale Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. By employing a local-inference strategy, our approach scales with linear complexity in the number of variables, efficiently scaling up to thousands of variables. Empirical results demonstrate superior performance in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning [7.365168184468284]
  We introduce a large-scale Genetic Variant dataset, named GV-Rep, featuring variable-length contexts and detailed annotations.<n> GV-Rep is designed for deep learning models to learn GV representations across various traits, diseases, tissue types, and experimental contexts.
  arXiv  Detail & Related papers  (2024-07-24T02:20:29Z)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification [109.81283748940696]
  We introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show that some simulation-based approaches are more robust (and accurate) than others for specific embedding methods to certain adversarial attacks to the input sequences.
  arXiv  Detail & Related papers  (2022-07-18T19:16:56Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.