Transformers in medical imaging: A survey

Shamshad, Fahad; Khan, Salman; Zamir, Syed Waqas; Khan, Muhammad Haris; Hayat, Munawar; Khan, Fahad Shahbaz; Fu, Huazhu

doi:10.1016/j.media.2023.102802

Cited by 381 publications

(97 citation statements)

References 167 publications

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“…Swin UNETR computes self-attention via an efficient shifting window partitioning algorithm and ranks first on the BraTs 2021 [ 38 ] validation set [ 48 ]. This approach, which is commonly used in medical imaging applications, is built on top of a SWin Transformer to extract and down-sample feature maps before feeding them into a Transformer [ 49 ].…”

Section: Segmentationmentioning

confidence: 99%

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Vinodkumar

Karabulut

Avots

et al. 2023

Entropy

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The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D object recognition models, along with evaluations of their distinctive qualities.

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Section: Segmentationmentioning

confidence: 99%

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Vinodkumar

Karabulut

Avots

et al. 2023

Entropy

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“…Transformer and its variants have been demonstrated to be effective in various vision tasks [22, 30–32]. For example, TNT [22] employs it for fine‐grained image tasks, where external and internal Transformers are used to extract global and local features.…”

Section: Related Workmentioning

confidence: 99%

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Guo

Fang

Wang

et al. 2023

IET Image Processing

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The large variations of polyp sizes and shapes and the close resemblances of polyps to their surroundings call for features with long-range information in rich scales and strong discrimination. This article proposes two parallel structured modules for building those features. One is the Transformer Inception module (TI) which applies Transformers with different reception fields in parallel to input features and thus enriches them with more long-range information in more scales. The other is the Local-Detail Augmentation module (LDA) which applies the spatial and channel attentions in parallel to each block and thus locally augments the features from two complementary dimensions for more object details. Integrating TI and LDA, a new Transformer encoder based framework, Parallel-Enhanced Network (PENet), is proposed, where LDA is specifically adopted twice in a coarse-tofine way for accurate prediction. PENet is efficient in segmenting polyps with different sizes and shapes without the interference from the background tissues. Experimental comparisons with state-of-the-arts methods show its merits.

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“…This has allowed for a more comprehensive and effective diagnosis of diseases, thereby improving human health care. Understanding the overall context between pixels against the background can help models avoid mis-segmentation [18].…”

Section: Applications Of Mismentioning

confidence: 99%

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

Dao¹,

Ly²

2023

IJACSA

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Over the past decade, Medical Image Segmentation (MIS) using Deep Neural Networks (DNNs) has achieved significant performance improvements and holds great promise for future developments. This paper presents a comprehensive study on MIS based on DNNs. Intelligent Vision Systems are often evaluated based on their output levels, such as Data, Information, Knowledge, Intelligence, and Wisdom (DIKIW), and the state-of-the-art solutions in MIS at these levels are the focus of research. Additionally, Explainable Artificial Intelligence (XAI) has become an important research direction, as it aims to uncover the "black box" nature of previous DNN architectures to meet the requirements of transparency and ethics. The study emphasizes the importance of MIS in disease diagnosis and early detection, particularly for increasing the survival rate of cancer patients through timely diagnosis. XAI and early prediction are considered two important steps in the journey from "intelligence" to "wisdom." Additionally, the paper addresses existing challenges and proposes potential solutions to enhance the efficiency of implementing DNN-based MIS.

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Transformers in medical imaging: A survey

Cited by 381 publications

References 167 publications

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

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