Automatically classifying the tissues types of Region of Interest (ROI) in medical imaging has been an important application in Computer-Aided Diagnosis (CAD), such as classification of breast parenchymal tissue in the mammogram, classify lung disease patterns in High-Resolution Computed Tomography (HRCT) etc. Recently, bag-of-features method has shown its power in this field, treating each ROI as a set of local features. In this paper, we investigate using the bag-of-features strategy to classify the tissue types in medical imaging applications. Two important issues are considered here: the visual vocabulary learning and weighting. Although there are already plenty of algorithms to deal with them, all of them treat them independently, namely, the vocabulary learned first and then the histogram weighted. Inspired by Auto-Context who learns the features and classifier jointly, we try to develop a novel algorithm that learns the vocabulary and weights jointly. The new algorithm, called Joint-ViVo, works in an iterative way. In each iteration, we first learn the weights for each visual word by maximizing the margin of ROI triplets, and then select the most discriminate visual words based on the learned weights for the next iteration. We test our algorithm on three tissue classification tasks: identifying brain tissue type in magnetic resonance imaging (MRI), classifying lung tissue in HRCT images, and classifying breast tissue density in mammograms. The results show that Joint-ViVo can perform effectively for classifying tissues.

In this paper, we propose a model for semantic clustering of entities extracted from a text, and we apply it to a Proper Noun classification task.This model is based on a new method to compute the similarity between the entities.Indeed, the classical way of calculating similarity is to build a feature vector or Bag-of-Features for each entity and then use classical similarity functions like Cosine.In practice, the features are contextual, such as words around the different occurrences of each entity. Here, we propose to use an alternative representation for entities, called Bag-of-Vectors, or Bag-of-Bags-of-Features.In this new model, each entity is not defined as a unique vector but as a set of vectors, in which each vector is built based on the contextual features of one occurrence of the entity.In order to use Bag-of-Vectors for clustering, we introduce new versions of classical similarity functions such as Cosine and Scalar Products. Experimentally, we show that the Bag-of-Vectors representation always improve the clustering results compared to classical Bag-of-Features representations.