A hybrid approach for improving U-Net variants in medical image segmentation

• URL: http://arxiv.org/abs/2307.16462v1
• Date: Mon, 31 Jul 2023 07:43:45 GMT
• Title: A hybrid approach for improving U-Net variants in medical image segmentation
• Authors: Aitik Gupta, Dr. Joydip Dhar
• Abstract summary: The technique of splitting a medical image into various segments or regions of interest is known as medical image segmentation. The segmented images that are produced can be used for many different things, including diagnosis, surgery planning, and therapy evaluation. This research aims to reduce the network parameter requirements using depthwise separable convolutions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation is vital to the area of medical imaging because it enables professionals to more accurately examine and understand the information offered by different imaging modalities. The technique of splitting a medical image into various segments or regions of interest is known as medical image segmentation. The segmented images that are produced can be used for many different things, including diagnosis, surgery planning, and therapy evaluation. In initial phase of research, major focus has been given to review existing deep-learning approaches, including researches like MultiResUNet, Attention U-Net, classical U-Net, and other variants. The attention feature vectors or maps dynamically add important weights to critical information, and most of these variants use these to increase accuracy, but the network parameter requirements are somewhat more stringent. They face certain problems such as overfitting, as their number of trainable parameters is very high, and so is their inference time. Therefore, the aim of this research is to reduce the network parameter requirements using depthwise separable convolutions, while maintaining performance over some medical image segmentation tasks such as skin lesion segmentation using attention system and residual connections.

Related papers

• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• A Recent Survey of Vision Transformers for Medical Image Segmentation [2.4895533667182703]
  Vision Transformers (ViTs) have emerged as a promising technique for addressing the challenges in medical image segmentation. Their multi-scale attention mechanism enables effective modeling of long-range dependencies between distant structures. Recently, researchers have come up with various ViT-based approaches that incorporate CNNs in their architectures, known as Hybrid Vision Transformers (HVTs)
  arXiv  Detail & Related papers  (2023-12-01T14:54:44Z)
• 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)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Few-shot Medical Image Segmentation with Cycle-resemblance Attention [20.986884555902183]
  Few-shot learning has gained increasing attention in the medical image semantic segmentation field. In this paper, we propose a novel self-supervised few-shot medical image segmentation network. We introduce a novel Cycle-Resemblance Attention (CRA) module to fully leverage the pixel-wise relation between query and support medical images.
  arXiv  Detail & Related papers  (2022-12-07T21:55:26Z)
• MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation [7.587725015524997]
  We propose a multi- kernel image segmentation net (MKIS-Net) MKIS-Net is a light-weight architecture with a small number of trainable parameters. We demonstrate the efficacy of MKIS-Net on several tasks including segmentation of retinal vessels, skin lesion segmentation, and chest X-ray segmentation.
  arXiv  Detail & Related papers  (2022-10-15T02:46:28Z)
• Medical Image Segmentation on MRI Images with Missing Modalities: A Review [3.9548535445908928]
  The main goal of this research is to offer a performance evaluation of missing modality compensating networks. Various approaches have been developed over time to mitigate this problem's negative implications.
  arXiv  Detail & Related papers  (2022-03-11T19:33:26Z)
• 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)
• Interactive Deep Refinement Network for Medical Image Segmentation [13.698408475104452]
  We propose an interactive deep refinement framework to improve the traditional semantic segmentation networks. In the proposed framework, we added a refinement network to traditional segmentation network to refine the results. Experimental results with public dataset revealed that the proposed method could achieve higher accuracy than other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-27T08:24:46Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

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.