By leveraging a pre-trained joint embedding space between images and text, our approach estimates a new classification task as a linear combination of words, resulting in a weight for each word that indicates its alignment with the vision-based classifier.
Breast cancer classification in mammography exemplifies these challenges, with a malignancy rate of around 0. 5% in a screening population, which is compounded by the relatively small size of lesions (~1% of the image) in malignant cases.
no code implementations • 23 Dec 2019 • William Lotter, Abdul Rahman Diab, Bryan Haslam, Jiye G. Kim, Giorgia Grisot, Eric Wu, Kevin Wu, Jorge Onieva Onieva, Jerrold L. Boxerman, Meiyun Wang, Mack Bandler, Gopal Vijayaraghavan, A. Gregory Sorensen
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018.
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.
Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial.
Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone.
Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world.