NEURO HAND: A weakly supervised Hierarchical Attention Network for interpretable neuroimaging abnormality Detection

• URL: http://arxiv.org/abs/2311.02992v2
• Date: Wed, 17 Jan 2024 01:56:27 GMT
• Title: NEURO HAND: A weakly supervised Hierarchical Attention Network for interpretable neuroimaging abnormality Detection
• Authors: David A. Wood
• Abstract summary: We present a hierarchical attention network for abnormality detection using MRI scans obtained in a clinical hospital setting. The proposed network is suitable for non-volumetric data (i.e. stacks of high-resolution MRI slices) and can be trained from binary examination-level labels.
• Score: 0.516706940452805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical neuroimaging data is naturally hierarchical. Different magnetic resonance imaging (MRI) sequences within a series, different slices covering the head, and different regions within each slice all confer different information. In this work we present a hierarchical attention network for abnormality detection using MRI scans obtained in a clinical hospital setting. The proposed network is suitable for non-volumetric data (i.e. stacks of high-resolution MRI slices), and can be trained from binary examination-level labels. We show that this hierarchical approach leads to improved classification, while providing interpretability through either coarse inter- and intra-slice abnormality localisation, or giving importance scores for different slices and sequences, making our model suitable for use as an automated triaging system in radiology departments.

Related papers

• Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks [7.734037486455235]
  We propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis. To the best of our knowledge, we are the first to develop an automated method for the 3D classification of MRI sequences in the chest, abdomen, and pelvis.
  arXiv  Detail & Related papers  (2024-02-12T22:34:57Z)
• CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation [5.662694302758443]
  Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. It frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition.
  arXiv  Detail & Related papers  (2023-09-06T19:01:58Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• SF2Former: Amyotrophic Lateral Sclerosis Identification From Multi-center MRI Data Using Spatial and Frequency Fusion Transformer [3.408266725482757]
  Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder involving motor neuron degeneration. Deep learning has turned into a prominent class of machine learning programs in computer vision. This study introduces a framework named SF2Former that leverages vision transformer architecture's power to distinguish the ALS subjects from the control group.
  arXiv  Detail & Related papers  (2023-02-21T18:16:20Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability [0.5772546394254112]
  This work employs a pre-trained, multi-view Convolutional Neural Network (CNN) with a spatial attention block to optimise knee injury detection. An open-source Magnetic Resonance Imaging (MRI) data set with image-level labels was leveraged for this analysis.
  arXiv  Detail & Related papers  (2021-08-18T13:24:17Z)
• Deep Learning-based Type Identification of Volumetric MRI Sequences [5.407839873345339]
  Unstandardized naming of MRI sequences makes their identification difficult for automated systems. We propose a system for identifying types of brain MRI sequences based on deep learning. Our system can classify among sequence types with an accuracy of 96.81%.
  arXiv  Detail & Related papers  (2021-06-06T18:34:47Z)
• 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)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.