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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).
We also demonstrate that a whole image classifier trained using our end-to-end approach on the DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations.
In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms.
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
The hyper-parameters used for all the classifiers were manually assigned.
We compare the two with respect to their robustness to Gaussian low-pass filtering, performing a subgroup analysis on microcalcifications and soft tissue lesions.
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.
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