Slicer Networks

• URL: http://arxiv.org/abs/2401.09833v1
• Date: Thu, 18 Jan 2024 09:50:26 GMT
• Title: Slicer Networks
• Authors: Hang Zhang, Xiang Chen, Rongguang Wang, Renjiu Hu, Dongdong Liu and Gaolei Li
• Abstract summary: We propose the Slicer Network, a novel architecture for medical image analysis. The Slicer Network strategically refines and upsamples feature maps via a splatting-blurring-slicing process. Experiments across different medical imaging applications have verified the Slicer Network's improved accuracy and efficiency.
• Score: 8.43960865813102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In medical imaging, scans often reveal objects with varied contrasts but consistent internal intensities or textures. This characteristic enables the use of low-frequency approximations for tasks such as segmentation and deformation field estimation. Yet, integrating this concept into neural network architectures for medical image analysis remains underexplored. In this paper, we propose the Slicer Network, a novel architecture designed to leverage these traits. Comprising an encoder utilizing models like vision transformers for feature extraction and a slicer employing a learnable bilateral grid, the Slicer Network strategically refines and upsamples feature maps via a splatting-blurring-slicing process. This introduces an edge-preserving low-frequency approximation for the network outcome, effectively enlarging the effective receptive field. The enhancement not only reduces computational complexity but also boosts overall performance. Experiments across different medical imaging applications, including unsupervised and keypoints-based image registration and lesion segmentation, have verified the Slicer Network's improved accuracy and efficiency.

Related papers

• MDFI-Net: Multiscale Differential Feature Interaction Network for Accurate Retinal Vessel Segmentation [3.152646316470194]
  This paper proposes a feature-enhanced interaction network based on DPCN, named MDFI-Net. The proposed MDFI-Net achieves segmentation performance superior to state-of-the-art methods on public datasets.
  arXiv  Detail & Related papers  (2024-10-20T16:42:22Z)
• LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention Network for Multifeatures Segmentation [15.091476025563528]
  Retinal diseases can cause irreversible vision loss in both eyes if not diagnosed and treated early. Current deep learning techniques for segmenting retinal images with many labels and attributes have poor detection accuracy and generalisability. This paper presents a multipath convolutional neural network for multifeature segmentation.
  arXiv  Detail & Related papers  (2024-07-03T07:37:09Z)
• Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures [3.386560551295746]
  This study investigates the effectiveness of neural network architectures in hyperspectral image demosaicing. We introduce a range of network models and modifications, and compare them with classical methods and existing reference network approaches. Results indicate that our networks outperform or match reference models in both datasets demonstrating exceptional performance.
  arXiv  Detail & Related papers  (2023-12-21T08:02:49Z)
• Multilayer Multiset Neuronal Networks -- MMNNs [55.2480439325792]
  The present work describes multilayer multiset neuronal networks incorporating two or more layers of coincidence similarity neurons. The work also explores the utilization of counter-prototype points, which are assigned to the image regions to be avoided.
  arXiv  Detail & Related papers  (2023-08-28T12:55:13Z)
• Deep Multi-Threshold Spiking-UNet for Image Processing [51.88730892920031]
  This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart.
  arXiv  Detail & Related papers  (2023-07-20T16:00:19Z)
• A Generic Shared Attention Mechanism for Various Backbone Neural Networks [53.36677373145012]
  Self-attention modules (SAMs) produce strongly correlated attention maps across different layers. Dense-and-Implicit Attention (DIA) shares SAMs across layers and employs a long short-term memory module. Our simple yet effective DIA can consistently enhance various network backbones.
  arXiv  Detail & Related papers  (2022-10-27T13:24:08Z)
• HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images [0.696194614504832]
  Medical image segmentation assists in computer-aided diagnosis, surgeries, and treatment. We proposed an generalization-Decoder Network, Quick Attention Module and a Multi Loss Function. We evaluate the capability of our proposed network on two publicly available datasets for medical image segmentation MoNuSeg and GlaS.
  arXiv  Detail & Related papers  (2022-09-01T21:10:00Z)
• FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks [77.34726150561087]
  We show how to explore high-dimensional landscape characteristics of neural networks. We generalize observations on small neural networks to more complex systems. An interactive dashboard opens up a number of possible application networks.
  arXiv  Detail & Related papers  (2022-04-09T16:41:53Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• HistoTransfer: Understanding Transfer Learning for Histopathology [9.231495418218813]
  We compare the performance of features extracted from networks trained on ImageNet and histopathology data. We investigate if features learned using more complex networks lead to gain in performance.
  arXiv  Detail & Related papers  (2021-06-13T18:55:23Z)
• Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound [74.22397862400177]
  We propose a novel Frustum ultrasound based catheter segmentation method. The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
  arXiv  Detail & Related papers  (2020-10-19T13:56:22Z)

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.