Assessment of algorithms for mitosis detection in breast cancer histopathology images

Abstract

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

arXiv comment23 pages, 5 figures, accepted for publication in the journal Medical Image Analysis

References in corpus (1)

• Manifold Relevance Determination

Cited by in corpus (35)

• Domain-adversarial neural networks to address the appearance variability of histopathology images
• Automatic segmentation of MR brain images with a convolutional neural network
• Deep neural network models for computational histopathology: A survey
• MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
• Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
• Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
• The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
• RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
• Mitosis domain generalization in histopathology images -- The MIDOG challenge
• Similar Image Search for Histopathology: SMILY
• Privacy-preserving Artificial Intelligence Techniques in Biomedicine
• A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
• Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
• A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research
• An interpretable machine learning system for colorectal cancer diagnosis from pathology slides
• Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists
• Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels
• Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing
• MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks
• Histopathologic Image Processing: A Review
• How Many Annotators Do We Need? -- A Study on the Influence of Inter-Observer Variability on the Reliability of Automatic Mitotic Figure Assessment
• Cancerous Nuclei Detection and Scoring in Breast Cancer Histopathological Images
• Deep Learning Models for Digital Pathology
• A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
• Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
• A Guided Spatial Transformer Network for Histology Cell Differentiation
• Skin lesion segmentation using U-Net and good training strategies
• HEp-2 Cell Image Classification with Deep Convolutional Neural Networks
• Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
• A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers
• Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images
• Mitosis Detection Under Limited Annotation: A Joint Learning Approach
• A Framework for Challenge Design: Insight and Deployment Challenges to Address Medical Image Analysis Problems
• Exploring the similarity of medical imaging classification problems
• Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer