Deep models for stroke segmentation: do complex architectures always perform better?

• URL: http://arxiv.org/abs/2403.17177v2
• Date: Sat, 16 Nov 2024 14:01:08 GMT
• Title: Deep models for stroke segmentation: do complex architectures always perform better?
• Authors: Yalda Zafari-Ghadim, Ahmed Soliman, Yousif Yousif, Ahmed Ibrahim, Essam A. Rashed, Mohamed Mabrok,
• Abstract summary: Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients. Deep models have been introduced for general medical image segmentation. In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation.
• Score: 1.4651272514940197
• License:
• Abstract: Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. Segmenting stroke lesions accurately is a challenging task, given that conventional manual techniques are time consuming and prone to errors. Recently, advanced deep models have been introduced for general medical image segmentation, demonstrating promising results that surpass many state of the art networks when evaluated on specific datasets. With the advent of the vision Transformers, several models have been introduced based on them, while others have aimed to design better modules based on traditional convolutional layers to extract long-range dependencies like Transformers. The question of whether such high-level designs are necessary for all segmentation cases to achieve the best results remains unanswered. In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well-known self-adaptive nnUNet framework with its configuration based on given data. We examined their performance on two publicly available datasets, and found that the nnUNet achieved the best results with the simplest design among all. Revealing the robustness issue of Transformers to such variabilities serves as a potential reason for their weaker performance. Furthermore, nnUNet's success underscores the significant impact of preprocessing and postprocessing techniques in enhancing segmentation results, surpassing the focus solely on architectural designs

Related papers

• Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation [10.746776960260297]
  We propose a new continual whole-body organ segmentation model with light-weighted low-rank adaptation (LoRA) We first train and freeze a pyramid vision transformer (PVT) base segmentation model on the initial task, then continually add light-weighted trainable LoRA parameters to the frozen model for each new learning task. Our proposed model continually segments new organs without catastrophic forgetting and meanwhile maintaining a low parameter increasing rate.
  arXiv  Detail & Related papers  (2024-10-07T02:00:13Z)
• LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models [59.961172635689664]
  "Knowledge Decomposition" aims to improve the performance on specific medical tasks. We propose a novel framework named Low-Rank Knowledge Decomposition (LoRKD) LoRKD explicitly separates gradients from different tasks by incorporating low-rank expert modules and efficient knowledge separation convolution.
  arXiv  Detail & Related papers  (2024-09-29T03:56:21Z)
• Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection [57.666055329221194]
  We investigate the adaptation of generic segmentation models, such as the Segment Anything Model (SAM), to infrared small object detection tasks. Our model demonstrates significantly improved performance in both accuracy and throughput compared to existing approaches.
  arXiv  Detail & Related papers  (2024-09-07T05:31:24Z)
• Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization [5.237321836999284]
  We train and evaluate five published models on the publicly available FIVES fundus image dataset. We find that image quality is a key factor determining segmentation outcomes.
  arXiv  Detail & Related papers  (2024-06-21T09:12:34Z)
• On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models [59.45628259925441]
  Volumetric medical segmentation models have achieved significant success on organ and tumor-based segmentation tasks. Their vulnerability to adversarial attacks remains largely unexplored. This underscores the importance of investigating the robustness of existing models.
  arXiv  Detail & Related papers  (2024-06-12T17:59:42Z)
• SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation [0.0]
  We propose a simple yet effective UNet-Transformer (seUNet-Trans) model for medical image segmentation. In our approach, the UNet model is designed as a feature extractor to generate multiple feature maps from the input images. By leveraging the UNet architecture and the self-attention mechanism, our model not only retains the preservation of both local and global context information but also is capable of capturing long-range dependencies between input elements.
  arXiv  Detail & Related papers  (2023-10-16T01:13:38Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• IterMiUnet: A lightweight architecture for automatic blood vessel segmentation [10.538564380139483]
  This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model. It overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.
  arXiv  Detail & Related papers  (2022-08-02T14:33:14Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Class-Aware Generative Adversarial Transformers for Medical Image Segmentation [39.14169989603906]
  We present CA-GANformer, a novel type of generative adversarial transformers, for medical image segmentation. First, we take advantage of the pyramid structure to construct multi-scale representations and handle multi-scale variations. We then design a novel class-aware transformer module to better learn the discriminative regions of objects with semantic structures.
  arXiv  Detail & Related papers  (2022-01-26T03:50:02Z)

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.