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Found 11 papers, 2 papers with code
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
The aim of this systematic review was to assess the performance of externally validated ML models based on histopathology images for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer.
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them.
Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review
We performed a PubMed search to find 148 papers published between January 2010 and December 2019 related to human brain, Diffusion Tensor Imaging (DTI), and Machine Learning (ML).
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets.
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
Ranked #2 on
Brain Segmentation
on Brain MRI segmentation
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
Then, based on the segmentation results, a subject-specific piecewise linear mapping function was applied between the anchor points to normalize the same type of tissue in different patients into the same intensity ranges.
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms.
Deep Learning for identifying radiogenomic associations in breast cancer
Results: The best AUC performance for distinguishing molecular subtypes was 0. 65 (95% CI:[0. 57, 0. 71]) and was achieved by the off-the-shelf deep features approach.
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
In the first approach, we adopted the transfer learning strategy, in which a network pre-trained on a large dataset of natural images is fine-tuned with our DCIS images.
Full-reference image quality assessment by combining global and local distortion measures
Therefore, we hypothesize that the resulting objective score for an image can be derived from the combination of local and global distortion measures calculated from the reference and test images.
High Frequency Content based Stimulus for Perceptual Sharpness Assessment in Natural Images
A blind approach to evaluate the perceptual sharpness present in a natural image is proposed.
