A systematic collection of medical image datasets for deep learning
The astounding success made by artificial intelligence in healthcare and other fields proves
that it can achieve human-like performance. However, success always comes with …
that it can achieve human-like performance. However, success always comes with …
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning
A Rączkowska, M Możejko, J Zambonelli… - Scientific reports, 2019 - nature.com
Abstract Machine learning algorithms hold the promise to effectively automate the analysis
of histopathological images that are routinely generated in clinical practice. Any machine …
of histopathological images that are routinely generated in clinical practice. Any machine …
Adaptive augmentation of medical data using independently conditional variational auto-encoders
M Pesteie, P Abolmaesumi… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Current deep supervised learning methods typically require large amounts of labeled data
for training. Since there is a significant cost associated with clinical data acquisition and …
for training. Since there is a significant cost associated with clinical data acquisition and …
Active learning for improved semi-supervised semantic segmentation in satellite images
S Desai, D Ghose - Proceedings of the IEEE/CVF winter …, 2022 - openaccess.thecvf.com
Remote sensing data is crucial for applications ranging from monitoring forest fires and
deforestation to tracking urbanization. Most of these tasks require dense pixel-level …
deforestation to tracking urbanization. Most of these tasks require dense pixel-level …
A review of machine learning approaches, challenges and prospects for computational tumor pathology
Computational pathology is part of precision oncology medicine. The integration of high-
throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics …
throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics …
Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
The splendid success of convolutional neural networks (CNNs) in computer vision is largely
attributable to the availability of massive annotated datasets, such as ImageNet and Places …
attributable to the availability of massive annotated datasets, such as ImageNet and Places …
Global context-aware cervical cell detection with soft scale anchor matching
Abstract Background and Objective: Computer-aided cervical cancer screening based on an
automated recognition of cervical cells has the potential to significantly reduce error rate and …
automated recognition of cervical cells has the potential to significantly reduce error rate and …
Intelligent labeling based on fisher information for medical image segmentation using deep learning
Deep convolutional neural networks (CNN) have recently achieved superior performance at
the task of medical image segmentation compared to classic models. However, training a …
the task of medical image segmentation compared to classic models. However, training a …
Pixel-to-pixel learning with weak supervision for single-stage nucleus recognition in Ki67 images
F Xing, TC Cornish, T Bennett… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Objective: Nucleus recognition is a critical yet challenging step in histopathology image
analysis, for example, in Ki67 immunohistochemistry stained images. Although many …
analysis, for example, in Ki67 immunohistochemistry stained images. Although many …
Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning
Breast mass detection is a challenging task in mammogram, since mass is usually
embedded and surrounded by various normal tissues with similar density. Recently, deep …
embedded and surrounded by various normal tissues with similar density. Recently, deep …