Deep Learning and Vision Transformer for Medical Image Analysis

Zhang, Yudong; Wang, Jiaji; Górriz, J. M.; Wang, Shuihua

doi:10.3390/jimaging9070147

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…While most previous investigations in medical imaging classification have used CNNs, a combined analysis of the PORTEC randomized trial and a clinical cohort conducted by Fremond et al [30] used attention-based models for class classification. Traditionally, CNNs have been widely used for image classification tasks; however, the introduction of the attention mechanism [53] has allowed for more accurate execution. CNNs capture relationships between adjacent pixels in images and recognize the content being displayed through structures called convolutional layers in the architecture.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

Umemoto,

Mariya,

Nambu

et al. 2024

Cancers

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The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

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Section: Discussionmentioning

confidence: 99%

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

Umemoto,

Mariya,

Nambu

et al. 2024

Cancers

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show abstract

“…For image data processing, visual transformers (ViTs) are a promising advancement. [3] Wu et al provide an exploration of vision transformers in the realm of ophthalmology, emphasizing their adaptability in processing varied image sizes. [4] Their insights suggest that when paired with CNNs, ViT models can surpass the capabilities of standalone alternatives.…”

Section: Ophthalmology's New Horizon: Moving From Reactive Care To Pr...mentioning

confidence: 99%

Ophthalmology’s new horizon: Moving from reactive care to proactive artificial intelligence solutions

Sevgi,

Keane

2023

Saudi Journal of Ophthalmology

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A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

Lubbad,

Kurtulus,

Karaboga

et al. 2024

J Digit Imaging. Inform. med.

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Deep Learning and Vision Transformer for Medical Image Analysis

Abstract: Artificial intelligence (AI) refers to the field of computer science theory and technology [...]

Cited by 11 publications

References 37 publications

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

Ophthalmology’s new horizon: Moving from reactive care to proactive artificial intelligence solutions

A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

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