An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Authors
Keywords
Deep learning, Breast cancer screening, Weakly supervised localization, High-resolution image classification
Journal
MEDICAL IMAGE ANALYSIS
Volume 68, Issue -, Pages 101908
Publisher
Elsevier BV
Online
2020-12-17
DOI
10.1016/j.media.2020.101908
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- International evaluation of an AI system for breast cancer screening
- (2020) Scott Mayer McKinney et al. NATURE
- Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification
- (2020) Xin Shu et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence
- (2019) Yiming Gao et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- Weakly supervised mitosis detection in breast histopathology images using concentric loss
- (2019) Chao Li et al. MEDICAL IMAGE ANALYSIS
- Attention gated networks: Learning to leverage salient regions in medical images
- (2019) Jo Schlemper et al. MEDICAL IMAGE ANALYSIS
- Constrained-CNN losses for weakly supervised segmentation
- (2019) Hoel Kervadec et al. MEDICAL IMAGE ANALYSIS
- Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
- (2019) Gabriele Campanella et al. NATURE MEDICINE
- Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives
- (2019) Krzysztof J. Geras et al. RADIOLOGY
- Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network
- (2019) Andrik Rampun et al. MEDICAL IMAGE ANALYSIS
- Deep Learning to Improve Breast Cancer Detection on Screening Mammography
- (2019) Li Shen et al. Scientific Reports
- Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks
- (2019) Muyi Sun et al. Cancers
- Deep learning in mammography and breast histology, an overview and future trends
- (2018) Azam Hamidinekoo et al. MEDICAL IMAGE ANALYSIS
- Detecting and classifying lesions in mammograms with Deep Learning
- (2018) Dezső Ribli et al. Scientific Reports
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
- (2018) Nicolas Coudray et al. NATURE MEDICINE
- Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement
- (2017) Philip Teare et al. JOURNAL OF DIGITAL IMAGING
- National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium
- (2017) Constance D. Lehman et al. RADIOLOGY
- A curated mammography data set for use in computer-aided detection and diagnosis research
- (2017) Rebecca Sawyer Lee et al. Scientific Data
- An open letter to panels that are deciding guidelines for breast cancer screening
- (2015) Daniel B. Kopans BREAST CANCER RESEARCH AND TREATMENT
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection
- (2015) Constance D. Lehman et al. JAMA Internal Medicine
- Weakly supervised histopathology cancer image segmentation and classification
- (2014) Yan Xu et al. MEDICAL IMAGE ANALYSIS
- Association of Computerized Mammographic Parenchymal Pattern Measure with Breast Cancer Risk: A Pilot Case-Control Study
- (2011) Jun Wei et al. RADIOLOGY
- A review of automatic mass detection and segmentation in mammographic images
- (2009) Arnau Oliver et al. MEDICAL IMAGE ANALYSIS
- Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features
- (2009) Matteo Masotti et al. MEDICAL PHYSICS
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started