The main claims of the paper [1] A certain level of localization labels are inevitable for WSOL. In fact, prior works that claim to be weakly supervised use strong supervision implicitly. Therefore, let’s standardize a protocol where the models are allowed to use pixel-level masks or bounding boxes to a limited degree. According to their proposed evaluation method, they have not observed any improvement in WSOL performances since CAM (2016) in this protocol.
In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles. Inspired by this, we seek to improve the performance of deep neural networks applied to this task by encouraging the model to use information …
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image …
Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and …
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.