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Found 10 papers, 0 papers with code
Improving Lesion Volume Measurements on Digital Mammograms
Finally, for a subset of 100 mammograms with a malign mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0. 81 [95% CI 0. 73 - 0. 87] for consistency and 0. 78 [95% CI 0. 66 - 0. 86] for absolute agreement.
Robust Cross-vendor Mammographic Texture Models Using Augmentation-based Domain Adaptation for Long-term Breast Cancer Risk
The texture model was combined with established risk factors to flag 10% of women with the highest risk.
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by: (1) noisy and expensive reference standards established by pathologists, (2) lack of generalization due to staining variation across laboratories, and (3) high computational requirements needed to process gigapixel whole-slide images (WSIs).
Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR
To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area.
Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks
We investigate the addition of symmetry and temporal context information to a deep Convolutional Neural Network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography.
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture.
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.
