Report on computational assessment of tumor infiltrating lymphocytes from the International Immuno-Oncology Biomarker Working Group

M Amgad, ES Stovgaard, E Balslev, J Thagaard… - NPJ breast …, 2020 - nature.com
Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral
part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer …

PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective

LAD Cooper, EG Demicco, JH Saltz… - The Journal of …, 2018 - Wiley Online Library
Abstract The Cancer Genome Atlas (TCGA) represents one of several international consortia
dedicated to performing comprehensive genomic and epigenomic analyses of selected …

Predicting cancer outcomes from histology and genomics using convolutional networks

P Mobadersany, S Yousefi, M Amgad… - Proceedings of the …, 2018 - pnas.org
Cancer histology reflects underlying molecular processes and disease progression and
contains rich phenotypic information that is predictive of patient outcomes. In this study, we …

[HTML][HTML] Artificial intelligence and digital microscopy applications in diagnostic hematopathology

H El Achi, JD Khoury - Cancers, 2020 - mdpi.com
Digital Pathology is the process of converting histology glass slides to digital images using
sophisticated computerized technology to facilitate acquisition, evaluation, storage, and …

The pathologist 2.0: an update on digital pathology in veterinary medicine

CA Bertram, R Klopfleisch - Veterinary pathology, 2017 - journals.sagepub.com
Using light microscopy to describe the microarchitecture of normal and diseased tissues has
changed very little since the middle of the 19th century. While the premise of histologic …

Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region

M Aubreville, CA Bertram, C Marzahl, C Gurtner… - Scientific reports, 2020 - nature.com
Manual count of mitotic figures, which is determined in the tumor region with the highest
mitotic activity, is a key parameter of most tumor grading schemes. It can be, however …

Automated diagnosis of lymphoma with digital pathology images using deep learning

H El Achi, T Belousova, L Chen, A Wahed… - Annals of Clinical & …, 2019 - annclinlabsci.org
Recent studies have shown promising results in using Deep Learning to detect malignancy
in whole slide imaging, however, they were limited to just predicting a positive or negative …

Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images

C Senaras, MKK Niazi, B Sahiner, MP Pennell… - PloS one, 2018 - journals.plos.org
In pathology, Immunohistochemical staining (IHC) of tissue sections is regularly used to
diagnose and grade malignant tumors. Typically, IHC stain interpretation is rendered by a …

An image analysis resource for cancer research: PIIP—Pathology image informatics platform for visualization, analysis, and management

AL Martel, D Hosseinzadeh, C Senaras, Y Zhou… - Cancer …, 2017 - aacrjournals.org
Abstract Pathology Image Informatics Platform (PIIP) is an NCI/NIH sponsored project
intended for managing, annotating, sharing, and quantitatively analyzing digital pathology …

What can machine vision do for lymphatic histopathology image analysis: a comprehensive review

H Chen, X Li, C Li, MM Rahaman, X Li, J Wu… - Artificial Intelligence …, 2024 - Springer
Over the past 10 years, machine vision (MV) algorithms for image analysis have been
develo** rapidly with computing power. At the same time, histopathological slices can be …