A new international competition aims to speed up the development of AI models that can assist radiologists in detecting suspicious lesions from hundreds of millions of pixels in 3D mammograms. The top three winning teams compare notes.
Ultrasound is an important imaging modality for the detection and characterization of breast cancer. Though consistently shown to detect mammographically occult cancers, especially in women with dense breasts, breast ultrasound has been noted to have …
Saliency maps that identify the most informative regions of an image for a classifier are valuable for model interpretability. A common approach to creating saliency maps involves generating input masks that mask out portions of an image to maximally …
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we …
During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that …
‘Processing Megapixel Images with Deep Attention-Sampling Models’ (referred as ‘ATS’ below) [1] proposes a new model that can save unnecessary computations from Deep MIL [2].
They first compute an attention map of all possible patch locations from an image. They do so by feeding a downsampled image to a shallow CNN without much pooling operations. They sample a small number of patches from the attention distribution and show that feeding these samplied patches to MIL classifier is an unbiased minimum-variance estimator of the prediction made with all patches.
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 …