Artificial intelligence in cancer imaging: clinical challenges and applications
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …
data with nuanced decision making. Cancer offers a unique context for medical decisions …
[HTML][HTML] Machine learning methods for histopathological image analysis
D Komura, S Ishikawa - Computational and structural biotechnology journal, 2018 - Elsevier
Abundant accumulation of digital histopathological images has led to the increased demand
for their analysis, such as computer-aided diagnosis using machine learning techniques …
for their analysis, such as computer-aided diagnosis using machine learning techniques …
Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer
This paper investigates a deep learning method in image classification for the detection of
colorectal cancer with ResNet architecture. The exceptional performance of a deep learning …
colorectal cancer with ResNet architecture. The exceptional performance of a deep learning …
Segmentation of nuclei in histopathology images by deep regression of the distance map
The advent of digital pathology provides us with the challenging opportunity to automatically
analyze whole slides of diseased tissue in order to derive quantitative profiles that can be …
analyze whole slides of diseased tissue in order to derive quantitative profiles that can be …
[HTML][HTML] Artificial intelligence and digital pathology: challenges and opportunities
In light of the recent success of artificial intelligence (AI) in computer vision applications,
many researchers and physicians expect that AI would be able to assist in many tasks in …
many researchers and physicians expect that AI would be able to assist in many tasks in …
[HTML][HTML] Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning
J Yang, J Ju, L Guo, B Ji, S Shi, Z Yang, S Gao… - Computational and …, 2022 - Elsevier
HER2-positive breast cancer is a highly heterogeneous tumor, and about 30% of patients
still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting …
still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting …
Multiple instance learning for histopathological breast cancer image classification
Histopathological images are the gold standard for breast cancer diagnosis. During
examination several dozens of them are acquired for a single patient. Conventional, image …
examination several dozens of them are acquired for a single patient. Conventional, image …
A review on a deep learning perspective in brain cancer classification
A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate
due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It …
due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It …
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
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 develo** and …
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly develo** and …