Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …
emerging that have enabled researchers to access multi-layer information from the genome …
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …
been successful at capturing image representations but their use has been generally …
[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives
NN Zhong, HQ Wang, XY Huang, ZZ Li, LM Cao… - Seminars in Cancer …, 2023 - Elsevier
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …
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 …
Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working …
J Thagaard, G Broeckx, DB Page… - The Journal of …, 2023 - Wiley Online Library
The clinical significance of the tumor‐immune interaction in breast cancer is now
established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and …
established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and …
A comprehensive survey on deep-learning-based breast cancer diagnosis
Simple Summary Breast cancer was diagnosed in 2.3 million women, and around 685,000
deaths from breast cancer were recorded globally in 2020, making it the most common …
deaths from breast cancer were recorded globally in 2020, making it the most common …
Spatial characterization of tumor-infiltrating lymphocytes and breast cancer progression
DJ Fassler, LA Torre-Healy, R Gupta, AM Hamilton… - Cancers, 2022 - mdpi.com
Simple Summary The assessment of tumor-infiltrating lymphocytes (TILs) is gaining
acceptance as a robust biomarker to help predict prognosis and treatment response. We …
acceptance as a robust biomarker to help predict prognosis and treatment response. We …
A systematic review on breast cancer detection using deep learning techniques
Breast cancer is a common health problem in women, with one out of eight women dying
from breast cancer. Many women ignore the need for breast cancer diagnosis as the …
from breast cancer. Many women ignore the need for breast cancer diagnosis as the …