Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings

• URL: http://arxiv.org/abs/2604.27195v1
• Date: Wed, 29 Apr 2026 20:55:50 GMT
• Title: Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings
• Abstract summary: We evaluate TabPFN against traditional machine learning methods for predicting 3 year to AD conversion.<n>At N=50 training samples, TabPFN maintained strong AUC while the traditional machine learning models struggles at small training samples.
• Score: 0.23923020933411976
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate prediction of conversion from Mild Cognitive Impairment (MCI) to Alzheimers Diseases (AD) is essential for early intervention, however, developing reliable conversion predictive models is difficult to develop due to limited longitudinal data availability We evaluate TabPFN (Tabular Pre-Trained Foundation Network) against traditional machine learning methods for predicting 3 year MCI to AD conversion using the TADPOLE dataset derived from ADNI. Using multimodal biomarker features extracted from demographics, APOE4, MRI volumes, CSF markers, and PET imaging, we conducted an experimental comparison across varying training set sizes (N=50 to 1000) and models including XGBoost, Random Forest, LightGBM, and Logistic Regression. TabPFN achieved one the highest performance (AUC=0.892), outperforming LightGBM (AUC=0.860) and demonstrating advantages in low data settings. At N=50 training samples, TabPFN maintained strong AUC while the traditional machine learning models struggles at small training samples. These findings demonstrate that foundation models are promising for disease prediction in data limited scenarios, such as Alzheimers diseases.

Related papers

• Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency [52.50039435394964]
  We systematically evaluate foundation models for regression-based tasks.<n>We extract patch-level features from whole slide images (WSI) using five state-of-the-art foundation models.<n>Models are trained to predict continuous HRD scores based on these extracted features across breast, endometrial, and lung cancer cohorts.
  arXiv  Detail & Related papers  (2026-01-29T14:06:50Z)
• Predicting Mycotoxin Contamination in Irish Oats Using Deep and Transfer Learning [0.0]
  Mycotoxin contamination poses a significant risk to cereal crop quality, food safety, and agricultural productivity.<n>This study investigates the use of neural networks and transfer learning models to predict mycotoxin contamination in Irish oat crops as a multi-response prediction task.
  arXiv  Detail & Related papers  (2025-12-23T20:08:50Z)
• Pretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimer's Disease Prediction [0.0]
  We propose a Transformer-based diagnostic framework that combines synthetic data generation with graph representation learning and transfer learning.<n>A class-conditional denoising diffusion probabilistic model (DDPM) is trained on the real-world NACC dataset to generate a large synthetic cohort.<n> Modality-specific Graph Transformer encoders are first pretrained on this synthetic data to learn robust, class-discriminative representations.
  arXiv  Detail & Related papers  (2025-11-24T19:34:53Z)
• MEG-GPT: A transformer-based foundation model for magnetoencephalography data [6.336623115095147]
  Recent advances in deep learning have enabled significant progress in other domains, such as language and vision, by using foundation models at scale.<n>Here, we introduce MEG-GPT, a transformer based foundation model that uses time-attention and next time-point prediction.<n>We trained MEG-GPT on tokenised brain region time-courses extracted from a large-scale MEG dataset.
  arXiv  Detail & Related papers  (2025-10-20T20:18:38Z)
• A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation [55.53426007439564]
  Estimating individualized treatment effects from observational data is a central challenge in causal inference.<n>In inverse probability weighting (IPW) is a well-established solution to this problem, but its integration into modern deep learning frameworks remains limited.<n>We propose Importance-Weighted Diffusion Distillation (IWDD), a novel generative framework that combines the pretraining of diffusion models with importance-weighted score distillation.
  arXiv  Detail & Related papers  (2025-05-16T17:00:52Z)
• Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data [2.1248439796866228]
  This study investigates pNML's learnability for linear regression and neural networks.<n>It demonstrates that pNML can improve the performance and robustness of these models on various tasks.
  arXiv  Detail & Related papers  (2024-12-10T13:58:19Z)
• Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices [0.0]
  Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability. A 5-minute prediction window was chosen for timely intervention, with minute-levels standardizing the data. This study highlights ML's potential to improve triage and reduce alarm fatigue.
  arXiv  Detail & Related papers  (2024-10-30T23:24:28Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

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.