Determining tumor cellularity in digital slides using ResNet-Reference-Cited by-同舟云学术

Determining tumor cellularity in digital slides using ResNet

Published:2018-03-06 Issue: Volume: Page:
ISSN:
Container-title:Medical Imaging 2018: Digital Pathology
language:
Short-container-title:

Author:

Akbar Shazia,Martel Anne L.,Peikari Mohammad,Salama Sherine,Nofech-Mozes Sharon

Publisher

SPIE

Reference21 articles.

1. Standardized Assessment of the HER2 Status in Breast Cancer by Immunohistochemistry

2. Gleason grading and prognostic factors in carcinoma of the prostate

3. Prognostic Significance of Nottingham Histologic Grade in Invasive Breast Carcinoma

4. Assessing tumor-infiltrating lymphocytes in solid tumors: A practical review for pathologists and proposal for a standardized method from the international immunooncology biomarkers working group: Part 1: Assessing the host immune response, tils in invasive breast carcinoma and ductal carcinoma in situ, metastatic tumor deposits and areas for further research;Hendry,2017

5. Creation of a fully digital pathology slide archive by high-volume tissue slide scanning

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Residual Tumor Cellularity Assessment of Breast Cancer After Neoadjuvant Therapy Using Image Transformer;IEEE Access;2024

2. Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning;Medical Image Analysis;2023-12

3. Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology;npj Breast Cancer;2023-04-06

4. RETRACTED ARTICLE: Periapical dental X-ray image classification using deep neural networks;Annals of Operations Research;2022-09-15

5. ULTRA: Uncertainty-Aware Label Distribution Learning for Breast Tumor Cellularity Assessment;Lecture Notes in Computer Science;2022