Multiclass Colorectal Cancer Histology Images Classification Using Vision Transformers

Zeid, Magdy Abd-Elghany; El-Bahnasy, Khaled; Abo-Youssef, S. E.

doi:10.1109/icicis52592.2021.9694125

Cited by 22 publications

(13 citation statements)

References 20 publications

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“…Second, the prediction accuracy of deep learning model for recognition of TME components should be improved, to ensure the calculated TME features are closer to the real situation. Some new models such as PDBL ( 31 ), CRCCN-Net ( 32 ) and Vision Transformer ( 33 ) are worthy of being using, since they have achieved accuracy of more than 96% in Kather’s dataset. Third, nearly half TME signature associated genes failed to be identified by GO and KEGG databases, which may affect the comprehensiveness of functional annotations for TME signature.…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

2023

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PurposeTo automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC).MethodsA deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.ResultsThe deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.ConclusionThe TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.

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Section: Discussionmentioning

confidence: 99%

Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

2023

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“…ViT architecture consists of Embedding Layer, Encoder and Final classi er head layers. [32]. The Vit architecture model is presented in Fig.…”

Section: Transformer Methodsmentioning

confidence: 99%

Achieving High Accuracy in Lemon Quality Classification: A Comparative Study of Deep Learning and Transformer Models

Dümen,

Yılmaz,

Adem

et al. 2023

Preprint

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Agricultural product quality assessment is important for the efficiency and marketability of production. Quality assessment improves industry standards, increasing sales and reducing crop loss. Maintaining quality is of paramount importance for all processes, from production to sales. Artificial intelligence has recently been frequently used for product quality assessment in the agricultural field. Both in the literature and in practice, deep learning and machine learning methods are used to process images of agricultural products and evaluate their quality. They are classified according to specified standards. In this study, firstly, data augmentation operations were performed on the lemon dataset consisting of two classes, bad quality and good quality, by using rescaling, random zoom, random flip, and random rotation methods. Afterward, eight different deep-learning methods and two different transformer methods were used for classification. As a result of the study calculated the most successful result as 99.84% accuracy, 99.95% recall, and 99.66% precision with the ViT method. This value is the highest accuracy value in the literature. When the experimental results are evaluated, it shows that lemon classification processes are successfully performed using the ViT method.

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“…In medical images, transformers have been applied in image classi cation, segmentation, detection, reconstruction, enhancement, and registration tasks 32 . Speci cally, in histological images, vision transformers have been successfully applied to different histological images related tasks, including in the detection of breast cancer metastases, and in the classi cation of cancer subtypes of lung, kidney and colorectal cancer 33,34 . Given the success of vision transformers in many medical applications and the capability of graph neural networks to capture correlation among patches, we adopt the combination of graph neural networks and Transformers to detect and classify BCCs.…”

Section: Introductionmentioning

confidence: 99%

Weakly supervised detection and classification of basal cell carcinoma using graph-transformers on whole slide images

Yacob¹,

Siarov

Villiamsson

et al. 2023

Preprint

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The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1831 WSIs from 479 BCCs, divided into training and validation (1434 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to 1) detect the presence of tumor (two classes), 2) classify the tumor into low and high-risk subtypes (three classes), and 3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency and possibly overcome inter-pathologist variability.

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Multiclass Colorectal Cancer Histology Images Classification Using Vision Transformers

Cited by 22 publications

References 20 publications

Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

Achieving High Accuracy in Lemon Quality Classification: A Comparative Study of Deep Learning and Transformer Models

Weakly supervised detection and classification of basal cell carcinoma using graph-transformers on whole slide images

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