Combining the Transformers and CNNs for Renal Parenchymal Tumors Diagnosis

Wang, Shuai; Zhang, Lian-Yu; Du, Huiqian; Chen, Yan; Zheng, Yao; Mei, Wenbo

doi:10.1109/icbcb55259.2022.9802126

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…However, because of its superior performance, CoAtNet has been applied in the medical domain. Wang et al [160] demonstrated that CoAtNet was effective in classifying renal parenchymal tumor subtypes and performed better than ViT. Kvak [161] used CoAtNet to classify malignant and benign melanoma and obtained an accuracy of 0.901, which is better than the accuracies obtained with other state-of-the-art algorithms.…”

Section: Potential Value Of Coatnet In Diffuse Liver Diseasesmentioning

confidence: 99%

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

Lei

et al. 2023

iRADIOLOGY

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The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

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Section: Potential Value Of Coatnet In Diffuse Liver Diseasesmentioning

confidence: 99%

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

Lei

et al. 2023

iRADIOLOGY

View full text Add to dashboard Cite

show abstract

Combining the Transformers and CNNs for Renal Parenchymal Tumors Diagnosis

Cited by 1 publication

References 16 publications

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

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