Search Results for author:
Found 8 papers, 4 papers with code
Representing visual classification as a linear combination of words
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.
Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
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.
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018.
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception
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.
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
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.
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
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.
Unsupervised Learning of Visual Structure using Predictive Generative Networks
The ability to predict future states of the environment is a central pillar of intelligence.
