w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution

• URL: http://arxiv.org/abs/2012.03674v1
• Date: Sun, 15 Nov 2020 13:54:22 GMT
• Title: w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution
• Authors: Bo Wang, Lei Wang, Junyang Chen, Zhenghua Xu, Thomas Lukasiewicz and Zhigang Fu
• Abstract summary: Multi-dimensional attention segmentation model with cascade multi-scale convolution is proposed to predict accurate segmentation for small objects in medical images. The proposed method is evaluated on three datasets: KiTS19, Pancreas CT of Decathlon-10, and MICCAI 2018 LiTS Challenge.
• Score: 47.56835064059436
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning-based medical image segmentation technology aims at automatic recognizing and annotating objects on the medical image. Non-local attention and feature learning by multi-scale methods are widely used to model network, which drives progress in medical image segmentation. However, those attention mechanism methods have weakly non-local receptive fields' strengthened connection for small objects in medical images. Then, the features of important small objects in abstract or coarse feature maps may be deserted, which leads to unsatisfactory performance. Moreover, the existing multi-scale methods only simply focus on different sizes of view, whose sparse multi-scale features collected are not abundant enough for small objects segmentation. In this work, a multi-dimensional attention segmentation model with cascade multi-scale convolution is proposed to predict accurate segmentation for small objects in medical images. As the weight function, multi-dimensional attention modules provide coefficient modification for significant/informative small objects features. Furthermore, The cascade multi-scale convolution modules in each skip-connection path are exploited to capture multi-scale features in different semantic depth. The proposed method is evaluated on three datasets: KiTS19, Pancreas CT of Decathlon-10, and MICCAI 2018 LiTS Challenge, demonstrating better segmentation performances than the state-of-the-art baselines.

Related papers

• Few-Shot Medical Image Segmentation with Large Kernel Attention [5.630842216128902]
  We propose a few-shot medical segmentation model that acquire comprehensive feature representation capabilities. Our model comprises four key modules: a dual-path feature extractor, an attention module, an adaptive prototype prediction module, and a multi-scale prediction fusion module. The results demonstrate that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-07-27T02:28:30Z)
• Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention [1.1155836879100416]
  We propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation. MFMSA block refines the process of spatial feature extraction, particularly in capturing boundary features. E-SDM mitigates information loss in multi-task learning with deep supervision.
  arXiv  Detail & Related papers  (2024-05-10T07:34:36Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images [50.76668288066681]
  We present a scale-aware super-resolution network to adaptively segment lesions of various sizes from low-resolution medical images. Our proposed network achieved consistent improvements compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-30T14:25:55Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
  Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations. AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
  arXiv  Detail & Related papers  (2022-02-18T10:14:45Z)
• (M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation [0.0]
  We propose a multi-scale, multi-level attention embedded CNN architecture ((M)SLAe-Net) to address the issue of multi-stage processing. We do this by extracting features at multiple scales and multiple levels of the network, enabling our model to holistically extracts the local and global features. Our unique network design and novel D-DPP module with efficient task-specific loss function for thin vessels enabled our model for better cross data performance.
  arXiv  Detail & Related papers  (2021-09-05T14:29:00Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Microscopic fine-grained instance classification through deep attention [7.50282814989294]
  Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging. We propose a simple yet effective deep network that performs two tasks simultaneously in an end-to-end manner. The result is a robust but lightweight end-to-end trainable deep network that yields state-of-the-art results.
  arXiv  Detail & Related papers  (2020-10-06T15:29:58Z)
• Boundary-aware Context Neural Network for Medical Image Segmentation [15.585851505721433]
  Medical image segmentation can provide reliable basis for further clinical analysis and disease diagnosis. Most existing CNNs-based methods produce unsatisfactory segmentation mask without accurate object boundaries. In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation.
  arXiv  Detail & Related papers  (2020-05-03T02:35:49Z)

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.