A survey on graph-based deep learning for computational histopathology
With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …
witnessed a rapid expansion of the use of machine learning and deep learning for the …
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 …
Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification
S Graham, M Jahanifar, A Azam… - Proceedings of the …, 2021 - openaccess.thecvf.com
The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Breast cancer is a heterogeneous disease with variable survival outcomes. Pathologists
grade the microscopic appearance of breast tissue using the Nottingham criteria, which are …
grade the microscopic appearance of breast tissue using the Nottingham criteria, which are …
[HTML][HTML] One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification
The recent surge in performance for image analysis of digitised pathology slides can largely
be attributed to the advances in deep learning. Deep models can be used to initially localise …
be attributed to the advances in deep learning. Deep models can be used to initially localise …
OCELOT: overlapped cell on tissue dataset for histopathology
Cell detection is a fundamental task in computational pathology that can be used for
extracting high-level medical information from whole-slide images. For accurate cell …
extracting high-level medical information from whole-slide images. For accurate cell …
Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review
Breast cancer in women is the second most common cancer worldwide. Early detection of
breast cancer can reduce the risk of human life. Non-invasive techniques such as …
breast cancer can reduce the risk of human life. Non-invasive techniques such as …
Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations
Recent advances in whole‐slide imaging (WSI) technology have led to the development of a
myriad of computer vision and artificial intelligence‐based diagnostic, prognostic, and …
myriad of computer vision and artificial intelligence‐based diagnostic, prognostic, and …
Unleashing the potential of AI for pathology: challenges and recommendations
Computational pathology is currently witnessing a surge in the development of AI
techniques, offering promise for achieving breakthroughs and significantly impacting the …
techniques, offering promise for achieving breakthroughs and significantly impacting the …
Interactive multi-class tiny-object detection
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a
multitude of Computer Vision tasks. Such imagery typically contains objects from various …
multitude of Computer Vision tasks. Such imagery typically contains objects from various …