DDxT: Deep Generative Transformer Models for Differential Diagnosis

• URL: http://arxiv.org/abs/2312.01242v1
• Date: Sat, 2 Dec 2023 22:57:25 GMT
• Title: DDxT: Deep Generative Transformer Models for Differential Diagnosis
• Authors: Mohammad Mahmudul Alam, Edward Raff, Tim Oates, Cynthia Matuszek
• Abstract summary: We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
• Score: 51.25660111437394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differential Diagnosis (DDx) is the process of identifying the most likely medical condition among the possible pathologies through the process of elimination based on evidence. An automated process that narrows a large set of pathologies down to the most likely pathologies will be of great importance. The primary prior works have relied on the Reinforcement Learning (RL) paradigm under the intuition that it aligns better with how physicians perform DDx. In this paper, we show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network. Experiments are performed using the DDXPlus dataset. In the case of DDx, the proposed network has achieved a mean accuracy of 99.82% and a mean F1 score of 0.9472. Additionally, mean accuracy reaches 99.98% with a mean F1 score of 0.9949 while predicting ground truth pathology. The proposed DDxT outperformed the previous RL-based approaches by a big margin. Overall, the automated Transformer-based DDx generative model has the potential to become a useful tool for a physician in times of urgency.

Related papers

• 1D-Convolutional transformer for Parkinson disease diagnosis from gait [7.213855322671065]
  This paper presents an efficient deep neural network model for diagnosing Parkinson's disease from gait. We introduce a hybrid ConvNetTransform-er architecture to accurately diagnose the disease by detecting the severity stage. Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data.
  arXiv  Detail & Related papers  (2023-11-06T15:17:17Z)
• An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation [0.3222802562733786]
  We leverage a deep learning-based method for the automatic diagnosis of schizophrenia using EEG brain recordings. This approach utilizes generative data augmentation, a powerful technique that enhances the accuracy of the diagnosis.
  arXiv  Detail & Related papers  (2023-10-25T12:55:16Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data [0.0]
  We propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN) scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Our experiments on seven public datasets validate that DTAAD exceeds the majority of currently advanced baseline methods in both detection and diagnostic performance.
  arXiv  Detail & Related papers  (2023-02-17T06:59:45Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings [23.777855250882244]
  Managing subjectivity in labels is a fundamental problem in medical imaging analysis. We introduce auto-decoded deep latent embeddings (ADDLE) ADDLE explicitly models the tendencies of each rater using an auto-decoder framework.
  arXiv  Detail & Related papers  (2021-04-12T15:40:42Z)
• Developing a New Autism Diagnosis Process Based on a Hybrid Deep Learning Architecture Through Analyzing Home Videos [1.2691047660244335]
  Currently, every 1 in 54 children have been diagnosed with Autism Spectrum Disorder (ASD), which is 178% higher than it was in 2000. We propose to develop a hybrid architecture using both categorical data and image data to automate traditional ASD pre-screening.
  arXiv  Detail & Related papers  (2021-04-02T17:30:35Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)

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.