Medical Diagnosis is the process of identifying the disease a patient is affected by, based on the assessment of specific risk factors, signs, symptoms and results of exams.
The toolkit aims to help both developers and researchers in the whole process of designing segmentation models, training models, optimizing performance and inference speed, and deploying models.
As a result, it exploits more discriminative features for DR grading.
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 paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights.
Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.
However it is unclear which OoDD method should be used in practice.
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
This function adds a weighted focal coefficient and combines two traditional loss functions.