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.
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 the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms.
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.