Cytometry. Part A : the journal of the International Society for Analytical Cytology
Oct 8, 2019
Building automated cancer screening systems based on image analysis is currently a hot topic in computer vision and medical imaging community. One of the biggest challenges of such systems, especially those using state-of-the-art deep learning techni...
We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast...
BACKGROUND: Early detection of early gastric cancer (EGC) allows for less invasive cancer treatment. However, differentiating EGC from gastritis remains challenging. Although magnifying endoscopy with narrow band imaging (ME-NBI) is useful for differ...
To heal otherwise in oncology has become an imperative of Public Health and an economic imperative in France. Patients can therefore receive live most of their care outside of hospital with more ambulatory care. This ambulatory shift will benefit fro...
We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the image level without any lesion delineation). Our proposed post-hoc metho...
Diagnostic and interventional imaging
Sep 12, 2019
The objective of this article was to evaluate the evidence currently available about the clinical value of artificial intelligence (AI) in breast imaging. Nine experts from the disciplines involved in breast disease management - including physicists ...
To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep c...
Traditionally, early esophageal cancer (i.e., cancer limited to the mucosa or superficial submucosa) was managed surgically; the gastroenterologist's role was primarily to diagnose the tumor. Over the last decade, advances in endoscopic imaging, abla...
The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screenin...
BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. The use of machine learning for the recognition and differentiation of images has been increasingly a...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.