Abstract
Two limitations hamper performance of deep architectures for classification and/or detection in medical imaging: (i) the small amount of available data, and (ii) the class imbalance scenario. While millions of labeled images are available today to build classification tools for natural scenes, the amount of available annotated data for automatic breast cancer screening is limited to a few thousand images, at best. We address these limitations with a method for data augmentation, based on the introduction of random elastic deformations on images of mammograms. We validate this method on three publicly available datasets. Our proposed Convolutional Neural Network (CNN) archi-tecture is trained for mass classification - in a conventional way - , and then used in the more interesting problem of mass detection in full mammograms by transforming the CNN into a Fully Convolutional Network (FCN). © 2018 IEEE.

Abstract

Abstract
Recent research efforts in semantic representations and context modeling are based on the principle of task expansion: that vision problems such as object recognition, scene classification, or retrieval (RCR) cannot be solved in isolation. The extended principle of modality expansion (that RCR problems cannot be solved from visual information alone) is investigated in this work. A semantic image labeling system is augmented with text. Pairs of images and text are mapped to a semantic space, and the text features used to regularize their image counterparts. This is done with a new cross-modal regularizer, which learns the mapping of the image features that maximizes their average similarity to those derived from text. The proposed regularizer is class-sensitive, combining a set of class-specific denoising transformations and nearest neighbor interpolation of text-based class assignments. Regularization of a state-of-the-art approach to image retrieval is then shown to produce substantial gains in retrieval accuracy, outperforming recent image retrieval approaches. © 2012 IEEE.

Abstract
The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling correlations between the two components, and 2) this modeling is more effective in feature spaces with higher levels of abstraction. Correlations between the two components are learned with canonical correlation analysis. Abstraction is achieved by representing text and images at a more general, semantic level. The two hypotheses are studied in the context of the task of cross-modal document retrieval. This includes retrieving the text that most closely matches a query image, or retrieving the images that most closely match a query text. It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy. The cross-modal model is also shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task. © 2010 ACM.