GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection

Chen, Haoyuan; Li, Chen; Wang, Ge; Li, Xiaoyan; Rahaman, Md Mamunur; Sun, Hongzan; Hu, Weiming; Li, Yixin; Liu, Wanli; Sun, Changhao; Ai, Shiliang; Grzegorzek, Marcin

doi:10.1016/j.patcog.2022.108827

Cited by 140 publications

(38 citation statements)

References 14 publications

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“…Besides, we can use MRF and CRF methods in [ 26 ] to mask the annotations of the various groups of imaging functions and regions of interest with the data augmentation. Furthermore, we can also apply the GasHisTransformer [ 27 ] to capture long-range correlation considering the global and local associations of the pulse signal in a unified context.…”

Section: Discussionmentioning

confidence: 99%

Pulse Signal Analysis Based on Deep Learning Network

Quanyu

2022

BioMed Research International

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Pulse signal is one of the most important physiological features of human body, which is caused by the cyclical contraction and diastole. It has great research value and broad application prospect in the detection of physiological parameters, the development of medical equipment, and the study of cardiovascular diseases and pulse diagnosis objective. In recent years, with the development of the sensor, measuring and saving of pulse signal has become very convenient. Now the pulse signal feature analysis is a hotspot and difficulty in the signal processing field. Therefore, to realize pulse signal automatic analysis and recognition is vital significance in the aspects of the noninvasive diagnosis and remote monitoring, etc. In this article, we combined the pulse signal feature extraction in time and frequency domain and convolution neural network to analyze the pulse signal. Firstly, a theory of wavelet transform and the ensemble empirical mode decomposition (EEMD) which is gradually developed in recent years have been used to remove the noises in the pulse signal. Moreover, a method of feature point detection based on differential threshold method is proposed which realized the accurate positioning and extraction time-domain values. Finally, a deep learning method based on one-dimensional CNN has been utilized to make the classification of multiple pulse signals in the article. In conclusion, a deep learning method is proposed for the pulse signal classification combined with the feature extraction in time and frequency domain in this article.

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Section: Discussionmentioning

confidence: 99%

Pulse Signal Analysis Based on Deep Learning Network

Quanyu

2022

BioMed Research International

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“…Medical imaging can provide full volume evaluation of the continuous nature of tumors by generating spatial resolution maps of subunits called "voxels" [23][24][25]. Malignant tumors have complex biology and show significant spatial variation in gene expression, biochemistry, histopathology, and macro structure.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

Wang

Sun

et al. 2022

BioMed Research International

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Purpose. We aim to develop and validate a machine learning model by enhanced MRI to determine the pathological grading of meningiomas with unsupervised clustering image analysis method, which are multihabitat to reflect the inherent heterogeneity of tumors. Materials and Methods. A total of 120 patients with meningiomas confirmed by postoperative pathology were included in the study, including 60 patients with low-grade meningiomas (WHO grade I) and 60 patients with high-grade meningiomas (WHO grade II and WHO grade III). All patients underwent complete head enhanced magnetic resonance scans before surgery or any anti-tumor treatment. Enrolled patients in the group received surgical resection and obtained postoperative pathological data. The patients in the training group (84 people) and the test group (36 people) were randomly divided into two groups according to the ratio of 7 to 3. Multi-habitat features were extracted from MRI images based on enhanced T1. Machine learning method was used to model, which was used to distinguish high-grade meningioma from low-grade meningioma. At the same time, the obtained machine learning model was calibrated and evaluated. Results. In patients with low-grade meningioma and high-grade meningioma, we found significant differences in Silhouette coefficient (P<0.05). In the machine learning model, the area under the curve was 0.838 in the training group (sensitivity, 67.65%; specificity, 88.82%) and 0.73 in the test group (sensitivity, 69.05%; specificity, 71.43%). After the analysis of calibration curve and decision curve analysis, the model had shown the potential of great application value. Conclusions. Multi-habitat analysis based on enhanced MRI (T1) could accurately predict the pathological grading of meningiomas. This unsupervised image-based method could reflect the direct heterogeneity between high-grade meningiomas and low-grade meningiomas, which is of great significance for patients’ treatment and prevention of recurrence.

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“…Attention MIL learns the contribution of individual tiles in the decision‐making process [137,140]. Most recently, transformers and graph neural networks that are intrinsically trained with the correlation information between different tiles along with the tile images have been proposed [134,141–143]. Another approach that is becoming more and more common in DL systems in histopathology is contrastive SSL, a subset of unsupervised learning [56,57,144,145].…”

Section: Perspectives and Outlookmentioning

confidence: 99%

Artificial intelligence to identify genetic alterations in conventional histopathology

Cifci

Foersch

Kather

2022

The Journal of Pathology

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Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53, and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows.

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GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection

Cited by 140 publications

References 14 publications

Pulse Signal Analysis Based on Deep Learning Network

Pulse Signal Analysis Based on Deep Learning Network

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

Artificial intelligence to identify genetic alterations in conventional histopathology

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