Transformers-based architectures for stroke segmentation: A review

• URL: http://arxiv.org/abs/2403.18637v1
• Date: Wed, 27 Mar 2024 14:42:08 GMT
• Title: Transformers-based architectures for stroke segmentation: A review
• Authors: Yalda Zafari-Ghadim, Essam A. Rashed, Mohamed Mabrok,
• Abstract summary: Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes. Transformers, initially designed for natural language processing, have exhibited remarkable capabilities in various computer vision applications, including medical image analysis. This review aims to provide an in-depth exploration of the cutting-edge Transformer-based architectures applied in the context of stroke segmentation.
• Score: 0.6554326244334866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes. The emergence of deep learning methodologies has transformed the landscape of medical image analysis. Recently, Transformers, initially designed for natural language processing, have exhibited remarkable capabilities in various computer vision applications, including medical image analysis. This comprehensive review aims to provide an in-depth exploration of the cutting-edge Transformer-based architectures applied in the context of stroke segmentation. It commences with an exploration of stroke pathology, imaging modalities, and the challenges associated with accurate diagnosis and segmentation. Subsequently, the review delves into the fundamental ideas of Transformers, offering detailed insights into their architectural intricacies and the underlying mechanisms that empower them to effectively capture complex spatial information within medical images. The existing literature is systematically categorized and analyzed, discussing various approaches that leverage Transformers for stroke segmentation. A critical assessment is provided, highlighting the strengths and limitations of these methods, including considerations of performance and computational efficiency. Additionally, this review explores potential avenues for future research and development

Related papers

• Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits [0.7968706282619793]
  Multi multiplexed imaging allows for simultaneous visualization of multiple biomarkers in a single section. Data from multiplexed imaging requires sophisticated computational methods for preprocessing, segmentation, feature extraction, and spatial analysis. PathML is an AI-powered platform that streamlines image analysis, making complex interpretation accessible for clinical and research settings.
  arXiv  Detail & Related papers  (2024-11-01T18:02:41Z)
• Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer [4.672688418357066]
  We propose a novel Transformer Diffusion (DTS) model for robust segmentation in the presence of noise. Our model, which analyzes the morphological representation of images, shows better results than the previous models in various medical imaging modalities.
  arXiv  Detail & Related papers  (2024-08-01T07:35:54Z)
• Anatomy-guided Pathology Segmentation [56.883822515800205]
  We develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods.
  arXiv  Detail & Related papers  (2024-07-08T11:44:15Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• Transformers in Healthcare: A Survey [11.189892739475633]
  Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks. We provide an overview of how this architecture has been adopted to analyze various forms of data, including medical imaging, structured and unstructured Electronic Health Records (EHR), social media, physiological signals, and biomolecular sequences. We discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.
  arXiv  Detail & Related papers  (2023-06-30T18:14:20Z)
• Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review [6.953789750981636]
  We provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks.
  arXiv  Detail & Related papers  (2023-01-09T16:56:23Z)
• Transformers in Medical Image Analysis: A Review [46.71636151229035]
  Our paper presents both a position paper and a primer, promoting awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we give a new taxonomy of various Transformer architectures tailored for medical image applications and discuss their limitations.
  arXiv  Detail & Related papers  (2022-02-24T16:04:03Z)
• Transformers in Medical Imaging: A Survey [88.03790310594533]
  Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results. Medical imaging has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. We provide a review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues.
  arXiv  Detail & Related papers  (2022-01-24T18:50:18Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Medical Transformer: Gated Axial-Attention for Medical Image Segmentation [73.98974074534497]
  We study the feasibility of using Transformer-based network architectures for medical image segmentation tasks. We propose a Gated Axial-Attention model which extends the existing architectures by introducing an additional control mechanism in the self-attention module. To train the model effectively on medical images, we propose a Local-Global training strategy (LoGo) which further improves the performance.
  arXiv  Detail & Related papers  (2021-02-21T18:35:14Z)

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.