Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction

• URL: http://arxiv.org/abs/2410.23649v1
• Date: Thu, 31 Oct 2024 05:40:08 GMT
• Title: Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction
• Authors: Guan-Hua Huang, Wan-Chen Lai, Tai-Been Chen, Chien-Chin Hsu, Huei-Yung Chen, Yi-Chen Wu, Li-Ren Yeh,
• Abstract summary: We developed a model capable of accurately predicting Parkinson's disease stages. We used the entire three-dimensional (3D) brain images as input. We incorporated an attention mechanism to account for the varying importance of different slices in the prediction process.
• Score: 2.931680194227131
• License:
• Abstract: Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures. Initially, we treated the 3D images as sequences of two-dimensional (2D) slices and fed them sequentially into 2D convolutional neural network (CNN) models pretrained on ImageNet, averaging the outputs to obtain the final predicted stage. We also applied 3D CNN models pretrained on Kinetics-400. Additionally, we incorporated an attention mechanism to account for the varying importance of different slices in the prediction process. To further enhance model efficacy and robustness, we simultaneously trained the two data sets using weight sharing, a technique known as cotraining. Our results demonstrated that 2D models pretrained on ImageNet outperformed 3D models pretrained on Kinetics-400, and models utilizing the attention mechanism outperformed both 2D and 3D models. The cotraining technique proved effective in improving model performance when the cotraining data sets were sufficiently large.

Related papers

• Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation [3.69758875412828]
  Cross-D Conv operation bridges the dimensional gap by learning the phase shifting in the Fourier domain. Our method enables seamless weight transfer between 2D and 3D convolution operations, effectively facilitating cross-dimensional learning.
  arXiv  Detail & Related papers  (2024-11-02T13:03:44Z)
• Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models [97.58685709663287]
  generative pre-training can boost the performance of fundamental models in 2D vision. In 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training. We propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
  arXiv  Detail & Related papers  (2023-07-27T16:07:03Z)
• Interpretable 2D Vision Models for 3D Medical Images [47.75089895500738]
  This study proposes a simple approach of adapting 2D networks with an intermediate feature representation for processing 3D images. We show on all 3D MedMNIST datasets as benchmark and two real-world datasets consisting of several hundred high-resolution CT or MRI scans that our approach performs on par with existing methods.
  arXiv  Detail & Related papers  (2023-07-13T08:27:09Z)
• Video Pretraining Advances 3D Deep Learning on Chest CT Tasks [63.879848037679224]
  Pretraining on large natural image classification datasets has aided model development on data-scarce 2D medical tasks. These 2D models have been surpassed by 3D models on 3D computer vision benchmarks. We show video pretraining for 3D models can enable higher performance on smaller datasets for 3D medical tasks.
  arXiv  Detail & Related papers  (2023-04-02T14:46:58Z)
• Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models [14.42534860640976]
  2D-Slice-CNN architecture embeds all the MRI slices with 2D encoders that take 2D image input and combines them via permutation-invariant layers. With the insight that pretrained model can serve as the 2D encoder, we initialize the 2D encoder with ImageNet pretrained weights that outperform those and trained from scratch on two neuroimaging tasks.
  arXiv  Detail & Related papers  (2023-03-02T18:52:31Z)
• Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition [25.80846093248797]
  We propose to process the 3D data by a 2+1D framework so that we can exploit the powerful deep 2D Convolutional Neural Network (CNN) networks pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition. Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics are decomposed to 2D slices according to neighboring voxel positions. Global pooling is applied to remove redundant information as the activation patterns are sparsely distributed over feature maps. Channel-wise and slice-wise convolutions are proposed to aggregate the contextual information in the third dimension unprocessed by the 2D CNN model.
  arXiv  Detail & Related papers  (2022-11-21T15:22:59Z)
• Medical Transformer: Universal Brain Encoder for 3D MRI Analysis [1.6287500717172143]
  Existing 3D-based methods have transferred the pre-trained models to downstream tasks. They demand a massive amount of parameters to train the model for 3D medical imaging. We propose a novel transfer learning framework, called Medical Transformer, that effectively models 3D volumetric images in the form of a sequence of 2D image slices.
  arXiv  Detail & Related papers  (2021-04-28T08:34:21Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
  We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
  arXiv  Detail & Related papers  (2020-07-09T13:23:15Z)
• 2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data [54.223614679807994]
  3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks. Applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D models. In this work, we proposed a novel 2D strategical representation of volumetric data, namely 2.75D. As a result, 2D CNN networks can also be used to learn volumetric information.
  arXiv  Detail & Related papers  (2020-02-11T08:24:19Z)

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.