MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

• URL: http://arxiv.org/abs/2102.13280v1
• Date: Fri, 26 Feb 2021 02:55:28 GMT
• Title: MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
• Authors: Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma and Yefeng Zheng
• Abstract summary: We propose a novel approach to mix small-scale datasets from multiple domains and segmentation tasks to produce a large-scale dataset. A novel encoder-decoder structure is designed to search for a generalized segmentation network in both cell-level and network-level. The network produced by the proposed MixSearch framework achieves state-of-the-art results compared with advanced encoder-decoder networks.
• Score: 37.232192775864576
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images. However, most Network Architecture Search (NAS) approaches in medical images focused on specific datasets and did not take into account the generalization ability of the learned architectures on unseen datasets as well as different domains. In this paper, we address this point by proposing to search for generalizable U-shape architectures on a composited dataset that mixes medical images from multiple segmentation tasks and domains creatively, which is named MixSearch. Specifically, we propose a novel approach to mix multiple small-scale datasets from multiple domains and segmentation tasks to produce a large-scale dataset. Then, a novel weaved encoder-decoder structure is designed to search for a generalized segmentation network in both cell-level and network-level. The network produced by the proposed MixSearch framework achieves state-of-the-art results compared with advanced encoder-decoder networks across various datasets.

Related papers

• Mixed-Query Transformer: A Unified Image Segmentation Architecture [57.32212654642384]
  Existing unified image segmentation models either employ a unified architecture across multiple tasks but use separate weights tailored to each dataset, or apply a single set of weights to multiple datasets but are limited to a single task. We introduce the Mixed-Query Transformer (MQ-Former), a unified architecture for multi-task and multi-dataset image segmentation using a single set of weights.
  arXiv  Detail & Related papers  (2024-04-06T01:54:17Z)
• 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)
• Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation [42.034896915716374]
  This paper presents a StyleGAN-driven approach for segmenting publicly available large medical datasets. Style transfer is used to augment the training dataset and generate new anatomically sound images. The augmented dataset is then used to train a U-Net segmentation network which displays a significant improvement in the segmentation accuracy.
  arXiv  Detail & Related papers  (2023-02-07T06:34:10Z)
• High-Quality Entity Segmentation [110.55724145851725]
  CropFormer is designed to tackle the intractability of instance-level segmentation on high-resolution images. It improves mask prediction by fusing high-res image crops that provide more fine-grained image details and the full image. With CropFormer, we achieve a significant AP gain of $1.9$ on the challenging entity segmentation task.
  arXiv  Detail & Related papers  (2022-11-10T18:58:22Z)
• HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images [0.0]
  We propose a hybrid NAS framework for ultrasound (US) image classification and segmentation. Our method can generate more powerful and lightweight models for the above US image classification and segmentation tasks.
  arXiv  Detail & Related papers  (2022-04-14T01:43:00Z)
• Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation [3.974175960216864]
  We show that image complexity can be used as a guideline in choosing what is best for a given dataset. For high-complexity datasets, a shallow network running on the original images may yield better segmentation results than a deep network running on downsampled images.
  arXiv  Detail & Related papers  (2021-12-21T09:49:47Z)
• DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets [96.92018649136217]
  We present a novel Domain-oriented Feature Embedding (DoFE) framework to improve the generalization ability of CNNs on unseen target domains. Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains. Our framework generates satisfying segmentation results on unseen datasets and surpasses other domain generalization and network regularization methods.
  arXiv  Detail & Related papers  (2020-10-13T07:28:39Z)
• Multi-Domain Image Completion for Random Missing Input Data [17.53581223279953]
  Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities. Due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources. We propose a general approach to complete the random missing domain(s) data in real applications.
  arXiv  Detail & Related papers  (2020-07-10T16:38:48Z)
• Universal Medical Image Segmentation using 3D Fabric Image Representation Encoding Networks [8.691611603448152]
  This work proposes one such network, Fabric Image Representation. Network (FIRENet), for simultaneous 3D multi-dataset segmentation. In this study, FIRENet was first applied to 3D universal bone segmentation involving multiple datasets of the human knee, shoulder and hip joints.
  arXiv  Detail & Related papers  (2020-06-28T11:35:23Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

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.