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Breast cancer is among the most deadly diseases, distressing mostly women worldwide.
The proposed method is a filter-based feature selection method, which directly utilises the Menger Curvature for ranking all the attributes in the given data set.
We compare the two with respect to their robustness to Gaussian low-pass filtering, performing a subgroup analysis on microcalcifications and soft tissue lesions.
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
3D MEDICAL IMAGING SEGMENTATION AUTOMATIC MACHINE LEARNING MODEL SELECTION BREAST CANCER DETECTION BREAST MASS SEGMENTATION IN WHOLE MAMMOGRAMS BREAST TUMOUR CLASSIFICATION INTERPRETABLE MACHINE LEARNING MATHEMATICAL PROOFS MEDICAL DIAGNOSIS MEDICAL IMAGE RETRIEVAL PROBABILISTIC DEEP LEARNING
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research.
Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making.
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.
The hyper-parameters used for all the classifiers were manually assigned.