Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
Deep learning in cancer pathology: a new generation of clinical biomarkers
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …
However, the growing number of these complex biomarkers tends to increase the cost and …
Developing image analysis methods for digital pathology
P Bankhead - The Journal of pathology, 2022 - Wiley Online Library
The potential to use quantitative image analysis and artificial intelligence is one of the
driving forces behind digital pathology. However, despite novel image analysis methods for …
driving forces behind digital pathology. However, despite novel image analysis methods for …
[HTML][HTML] Deep learning in computational dermatopathology of melanoma: A technical systematic literature review
Deep learning (DL) has become one of the major approaches in computational
dermatopathology, evidenced by a significant increase in this topic in the current literature …
dermatopathology, evidenced by a significant increase in this topic in the current literature …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
Squeeze-mnet: Precise skin cancer detection model for low computing IOT devices using transfer learning
Simple Summary Skin cancer is a life-threatening condition. It is difficult to diagnose in its
early stages; therefore, we proposed an easy-to-use telemedicine device to tackle skin …
early stages; therefore, we proposed an easy-to-use telemedicine device to tackle skin …
Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images
We developed end-to-end deep learning models using whole slide images of adults
diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to …
diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to …
Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images
Z Wu, Y Yang, M Chen, Y Zha - Scientific Reports, 2024 - nature.com
This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9)
expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms …
expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms …
Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: A cancer case survey
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite
playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this …
playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this …
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
JP Redlich, F Feuerhake, J Weis, NS Schaadt… - npj Imaging, 2024 - nature.com
In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of
glioma histopathology images using artificial intelligence (AI) offers new opportunities to …
glioma histopathology images using artificial intelligence (AI) offers new opportunities to …