Artificial intelligence-based tool for tumor detection and quantitative tissue analysis in colorectal specimens.

[1]  A. Mukhopadhyay,et al.  Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study. , 2023, The Lancet. Digital health.

[2]  A. Jemal,et al.  Cancer statistics, 2023 , 2023, CA: a cancer journal for clinicians.

[3]  L. Le Marchand,et al.  Quantitative pathologic analysis of digitized images of colorectal carcinoma improves prediction of recurrence free survival. , 2022, Gastroenterology.

[4]  A. Tzallas,et al.  Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review , 2022, Diagnostics.

[5]  Zitong Zhao,et al.  A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer , 2022, Scientific Reports.

[6]  S. Hassanpour,et al.  Evaluation of an Artificial Intelligence–Augmented Digital System for Histologic Classification of Colorectal Polyps , 2021, JAMA network open.

[7]  L. Solorzano,et al.  Improved breast cancer histological grading using deep learning. , 2021, Annals of oncology : official journal of the European Society for Medical Oncology.

[8]  Djeane Debora Onthoni,et al.  Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning , 2021, Diagnostics.

[9]  Jakob Nikolas Kather,et al.  Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study , 2021, The Lancet. Digital health.

[10]  A. Madabhushi,et al.  Quality control stress test for deep learning-based diagnostic model in digital pathology , 2021, Modern Pathology.

[11]  D. Rimm,et al.  An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy , 2021, Modern Pathology.

[12]  R. Pai,et al.  Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters , 2021, Histopathology.

[13]  Jakob Nikolas Kather,et al.  Deep learning in cancer pathology: a new generation of clinical biomarkers , 2020, British Journal of Cancer.

[14]  Jing Yuan,et al.  Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists , 2020, BMJ Open.

[15]  Liron Pantanowitz,et al.  An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. , 2020, The Lancet. Digital health.

[16]  Chao Xu,et al.  Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images , 2020, Nature Communications.

[17]  Yuri Tolkach,et al.  High-accuracy prostate cancer pathology using deep learning , 2020, Nature Machine Intelligence.

[18]  Elizabeth L. Barry,et al.  Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides , 2020, JAMA network open.

[19]  J. Yun,et al.  Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence , 2020, bioRxiv.

[20]  A. Madabhushi,et al.  Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology , 2019, Nature Reviews Clinical Oncology.

[21]  Thomas J. Fuchs,et al.  Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.

[22]  Geert J. S. Litjens,et al.  Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology , 2019, Medical Image Anal..

[23]  N. Razavian,et al.  Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning , 2018, Nature Medicine.

[24]  Hao Chen,et al.  MILD‐Net: Minimal information loss dilated network for gland instance segmentation in colon histology images , 2018, Medical Image Anal..

[25]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[26]  David B. A. Epstein,et al.  Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images , 2017, Scientific Reports.

[27]  Nasir M. Rajpoot,et al.  A Stochastic Polygons Model for Glandular Structures in Colon Histology Images , 2015, IEEE Transactions on Medical Imaging.

[28]  Germain Forestier,et al.  Postdoc position: Deep learning for colon cancer histopathological images analysis , 2020 .