Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
Metastatic presence in lymph nodes is one of the most important prognostic variables of breast cancer. The current diagnostic procedure for manually reviewing sentinel lymph nodes, however, is very time-consuming and subjective. Pathologists have to manually scan an entire digital whole-slide image (WSI) for regions of metastasis that are sometimes only detectable under high resolution or entirely hidden from the human visual cortex. From October 2015 to April 2016, the International Symposium on Biomedical Imaging (ISBI) held the Camelyon Grand Challenge 2016 to crowd-source ideas and algorithms for automatic detection of lymph node metastasis. Using a generalizable stain normalization technique and the Proscia Pathology Cloud computing platform, we trained a deep convolutional neural network on millions of tissue and tumor image tiles to perform slide-based evaluation on our testing set of whole-slide images images, with a sensitivity of 0.96, specificity of 0.89, and AUC score of 0.90. Our results indicate that our platform can automatically scan any WSI for metastatic regions without institutional calibration to respective stain profiles.
Code (0)
등록된 구현이 없습니다.
Tasks
Cloud ComputingDiagnosticSpecificitywhole slide imagesSimilar Papers 제목 키워드 기반
Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes
Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and analysis of all the lymph nodes slides for…
AllDeep LearningDiagnosticwhole slide imagesSemi-Supervised Learning for Cancer Detection of Lymph Node Metastases
Pathologists find tedious to examine the status of the sentinel lymph node on a large number of pathological scans. The examination process of such lymph node which encompasses metastasized cancer cells is histopathologi…
Domain adaptation strategies for cancer-independent detection of lymph node metastases
Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardl…
Cancer Metastasis DetectionDomain AdaptationCluster-Based Learning from Weakly Labeled Bags in Digital Pathology
To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in microscopic images of breast lymph nodes. W…
Weakly-supervised LearningEfficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
Successful clinical implementation of deep learning in medical imaging depends, in part, on the reliability of the predictions. Specifically, the system should be accurate for classes seen during training while providing…
Out-of-Distribution DetectionOut of Distribution (OOD) Detection