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). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the …
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses. To reflect this practice, we propose new neural network models that compare pairs of screening mammograms from …
Visualization of ultrasound classifier The challenge was to interpret the performance of inception v1 network on their ultrasound images gathered from using Butterfly hand-held ultrasound devices. We utilized a simple method of erasing parts of images, feeding them to the classifier, observing the class probability of the correct class. White means higher value of class probability, meaning the model was more sure of its prediction when that particular region was removed.