4.6 Article

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Medicine, Research & Experimental

Where Position Matters-Deep-Learning- Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation

Marco Reisert et al.

Summary: Recent developments in deep brain stimulation surgery have led to the need for accurate detection of achieved electrode positions based on postoperative imaging. A deep-learning approach was used to directly relate the positions in postoperative CT images to the native anatomy of the midbrain and group space.

NEUROMODULATION (2023)

Article Health Care Sciences & Services

SVM classifier of cervical histopathology images based on texture and morphological features

Siqi He et al.

Summary: This study aims to identify early-stage cervical cancer histopathology images by extracting texture and morphological features and using the Support Vector Machine (SVM) classifier. The experiment used an original dataset from 20 cervical cancer patients, including 135 whole slide images (WSIs). The results show that the SVM classification accuracy using different feature extraction methods ranges from 87.4% to 93.5%. Combining these features, the SVM classification accuracy improves to 96.8%, with the nucleus feature playing a key role.

TECHNOLOGY AND HEALTH CARE (2023)

Article Biology

Multi-Planar VMAT Plans for High-Grade Glioma and Glioblastoma Targeting the Hypothalamic-Pituitary Axis Sparing

Eva Y. W. Cheung et al.

Summary: This study aimed to identify the better arc configuration of VMAT for high-grade glioma and glioblastoma by analyzing dose-volumetric parameters and the correlation between PTV-OAR distance and radiation dose. The results showed that DP-VMAT and MP-VMAT achieved significant dose reductions to nearby OARs without compromising the dose to PTV, plan homogeneity and conformity.

LIFE-BASEL (2022)

Article Multidisciplinary Sciences

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix

Yan Hao et al.

Summary: This paper proposes a method for breast cancer histopathological image recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features. The experimental results show that this method performs better than pretrained baseline models in terms of image-level and patient-level recognition accuracy. The method also compares favorably with state-of-the-art methods.

PLOS ONE (2022)

Article Biology

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

Eva Y. W. Cheung et al.

Summary: This study aimed to build automated detection models using brain regional volume and radiomics features to differentiate mild cognitive impairment, cognitive normal, and Alzheimer's Disease. The study found that radiomics features achieved excellent performance in differentiating the three stages of neurodegenerations.

LIFE-BASEL (2022)

Review Genetics & Heredity

Applications of machine learning in metabolomics: Disease modeling and classification

Aya Galal et al.

Summary: Metabolomics research allows direct insights into cellular or tissue states, and machine learning techniques enhance disease modeling and diagnosis by analyzing and modeling complex metabolomic data.

FRONTIERS IN GENETICS (2022)

Article Multidisciplinary Sciences

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix

Yan Hao et al.

Summary: A method of breast cancer histopathological image recognition based on deep semantic features and GLCM features is proposed, showing better performance than baseline models and of great significance for improving diagnostic accuracy.

PLOS ONE (2022)

Article Oncology

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Hanya Mahmood et al.

Summary: This paper reviews recent literature on the use of Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) through automated image analysis. The studies show that these methods can achieve high accuracy in detection by using various imaging modalities.

BRITISH JOURNAL OF CANCER (2021)

Article Oncology

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

Yan Hao et al.

Summary: The paper introduces a method based on low-dimensional three-channel features for breast cancer histopathological image recognition, which has been proven to achieve high accuracy in distinguishing between benign and malignant breast cancer, outperforming many state-of-the-art methods.

FRONTIERS IN ONCOLOGY (2021)

Article Oncology

Artificial Intelligence in Ovarian Cancer Diagnosis

Munetoshi Akazawa et al.

ANTICANCER RESEARCH (2020)

Review Health Care Sciences & Services

Lung Cancer Screening with Low-Dose CT: a Meta-Analysis

Richard M. Hoffman et al.

JOURNAL OF GENERAL INTERNAL MEDICINE (2020)

Article Statistics & Probability

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

Xiao Li et al.

JOURNAL OF APPLIED STATISTICS (2019)

Review Clinical Neurology

Current and future strategies for treatment of glioma

Nancy Ann Oberheim Bush et al.

NEUROSURGICAL REVIEW (2017)

Article Multidisciplinary Sciences

SVM and SVM Ensembles in Breast Cancer Prediction

Min-Wei Huang et al.

PLOS ONE (2017)

Article Multidisciplinary Sciences

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

Yucheng Zhang et al.

SCIENTIFIC REPORTS (2017)

Editorial Material Computer Science, Artificial Intelligence

Image analysis and machine learning in digital pathology: Challenges and opportunities

Anant Madabhushi et al.

MEDICAL IMAGE ANALYSIS (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

Kenneth Clark et al.

JOURNAL OF DIGITAL IMAGING (2013)

Article Computer Science, Artificial Intelligence

A high-throughput active contour scheme for segmentation of histopathological imagery

Jun Xu et al.

MEDICAL IMAGE ANALYSIS (2011)

Review Clinical Neurology

The 2007 WHO classification of tumours of the central nervous system

David N. Louis et al.

ACTA NEUROPATHOLOGICA (2007)

Article Remote Sensing

An analysis of co-occurrence texture statistics as a function of grey level quantization

DA Clausi

CANADIAN JOURNAL OF REMOTE SENSING (2002)