Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification

• URL: http://arxiv.org/abs/2203.08408v1
• Date: Wed, 16 Mar 2022 05:51:16 GMT
• Title: Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
• Authors: Zifan Chen, Jie Zhao, Hao Yu, Yue Zhang, Li Zhang
• Abstract summary: This work introduces a multi-scale context-guided network with coarse-to-fine localization and classification, named CCF-Net, for lumbar spine disease identification. The experimental results show that the coarse-to-fine design presents the potential to achieve high performance with fewer parameters and data requirements.
• Score: 22.62393344071125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis. However, existing deep learning models with millions of parameters often fail to learn with only hundreds or dozens of medical images. These models also ignore the contextual relationship between adjacent objects, such as between vertebras and intervertebral discs. This work introduces a multi-scale context-guided network with coarse-to-fine localization and classification, named CCF-Net, for lumbar spine disease identification. Specifically, in learning, we divide the localization objective into two parallel tasks, coarse and fine, which are more straightforward and effectively reduce the number of parameters and computational cost. The experimental results show that the coarse-to-fine design presents the potential to achieve high performance with fewer parameters and data requirements. Moreover, the multi-scale context-guided module can significantly improve the performance by 6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is available at https://github.com/czifan/CCFNet.pytorch.

Related papers

• LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis [1.347667211255822]
  This paper presents a novel lightweight encoder-decoder model that segments retinal vessels to improve the efficiency of disease detection. It incorporates multi-scale convolutional blocks in the encoder to accurately identify vessels of various sizes and thicknesses. It outperforms existing models with dice scores of 86.44%, 84.22%, and 87.88%, respectively.
  arXiv  Detail & Related papers  (2024-12-08T15:21:37Z)
• Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding [51.38395069380457]
  Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability. In this study, we specifically explore the use of shape auto-encoders for vertebrae.
  arXiv  Detail & Related papers  (2023-12-08T18:11:22Z)
• LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation [0.0]
  Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images. Deep learning has shown promise in medical image segmentation, but its reliance on repeated convolution and pooling operations can hinder the representation of edge information. We propose a lightweight pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an exceptionally low number of learnable parameters.
  arXiv  Detail & Related papers  (2023-09-10T09:03:53Z)
• An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification [0.5424799109837065]
  This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model.
  arXiv  Detail & Related papers  (2022-04-21T06:36:10Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation [0.0]
  We propose a novel Dimension Fusion Edge-guided network (DFENet) that can meet both of these requirements by fusing the features of 2D and 3D CNNs. The proposed model is robust, accurate, superior to the existing methods, and can be relied upon for biomedical applications.
  arXiv  Detail & Related papers  (2021-05-17T15:43:59Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• 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)
• VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI [30.469124322749828]
  We present a subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) Compared with popular convolutional neural network (CNN) architectures, VoxelHop has modular and transparent structures with fewer parameters without any backpropagation. Our framework can easily be generalized to other classification tasks using different imaging modalities.
  arXiv  Detail & Related papers  (2021-01-13T15:25:57Z)
• 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)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)

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.