Deep learning with radiomics for disease diagnosis and treatment: challenges and potential
The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …
Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
Lung cancer has been the most common and the leading cause of cancer deaths globally.
Besides clinicopathological observations and traditional molecular tests, the advent of …
Besides clinicopathological observations and traditional molecular tests, the advent of …
Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning
Every year cervical cancer affects more than 300,000 people, and on average one woman is
diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical …
diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical …
A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images
Cancer region detection (CRD) and subtyping are two fundamental tasks in digital pathology
image analysis. The development of data-driven models for CRD and subtyping on whole …
image analysis. The development of data-driven models for CRD and subtyping on whole …
Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images
Despite the progress made during the last two decades in the surgery and chemotherapy of
ovarian cancer, more than 70% of advanced patients are with recurrent cancer and …
ovarian cancer, more than 70% of advanced patients are with recurrent cancer and …
[HTML][HTML] Generative models for color normalization in digital pathology and dermatology: Advancing the learning paradigm
Color medical images introduce an additional confounding factor compared to conventional
grayscale medical images: color variability. This variability can lead to inconsistent …
grayscale medical images: color variability. This variability can lead to inconsistent …
A weakly supervised deep learning method for guiding ovarian cancer treatment and identifying an effective biomarker
Simple Summary Molecular target therapy, ie, antiangiogenesis with bevacizumab, was
found to be effective in some patients of epithelial ovarian cancer. Considering the cost …
found to be effective in some patients of epithelial ovarian cancer. Considering the cost …
Cancer detection and segmentation using machine learning and deep learning techniques: A review
HM Rai - Multimedia Tools and Applications, 2024 - Springer
Cancer is the most fatal diseases in the world which has highest mortality rate as compared
to other type's human diseases. The most common and dangerous types of cancers are lung …
to other type's human diseases. The most common and dangerous types of cancers are lung …
Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images
Y Jiao, J Li, C Qian, S Fei - Computer Methods and Programs in …, 2021 - Elsevier
Abstract Background and Objective: Colon cancer is a fatal disease, and a comprehensive
understanding of the tumor microenvironment (TME) could lead to better risk stratification …
understanding of the tumor microenvironment (TME) could lead to better risk stratification …
Deep machine learning for medical diagnosis, application to lung cancer detection: a review
HT Gayap, MA Akhloufi - BioMedInformatics, 2024 - mdpi.com
Deep learning has emerged as a powerful tool for medical image analysis and diagnosis,
demonstrating high performance on tasks such as cancer detection. This literature review …
demonstrating high performance on tasks such as cancer detection. This literature review …