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In this work we analyze the impact of denoising, contrast and edge enhancement using the Deceived Non Local Means (DNLM) filter in a Convolutional Neural Network (CNN) based approach for age estimation using digital X-ray images from hands. The DNLM filter contains two parameters which control edge enhancement and denoising. Increasing levels were tested to assess the impact of both contrast enhancement and denoising in the CNN based model regression accuracy.

Numerous recent papers have demonstrated the utility of graph theoretical analysis in conjunction with sparse inverse covariance estimation (SICE) in understanding the modulation of brain connectivity associated with neuropathology. These concepts may complement established knowledge of functional covariance obtained using principal component analysis (PCA) that can reduce whole data representations of brain data to essential disease specific patterns.

We focus on first-person action recognition from egocentric videos. Unlike third person domain, researchers have divided first-person actions into two categories: involving hand-object interactions and the ones without, and developed separate techniques for the two action categories. Further, it has been argued that traditional cues used for third person action recognition do not suffice, and egocentric specific features, such as head motion and handled objects have been used for such actions.

We propose a novel method for content-based image retrieval based on the features extracted from the convolutional layers of the deep neural network architecture. Some of the popular approaches form the feature vectors from the fully connected layers of the convolutional neural networks or directly concatenate the features from the convolutional layers. However, the main problem with the use of feature vectors from fully connected layers is that the spatial information about the objects is lost. This motivated us to use the features from the convolutional layer.

Cross-modal retrieval is a hot topic in the fields of machine learning and media retrieval, making it possible to relate different types of media, such as image, text, and audio. A powerful method for the cross-modal retrieval, discrete cross-modal hashing (DCH), has recently been proposed. The DCH can encode different types of media feature vectors to binary codes. When stored in a database, the binary code makes searches efficient because the Hamming distance between the corresponding sections of two binary codes can be computed via a specialized CPU operation.

Computer-aided diagnosis (CAD) has great potential in providing real benefits to doctors and patients. Recent studies have, however, found lack of trust in CAD by radiologists in clinical diagnostic decision making. One of the main reasons is the lack of an appropriate confidence measure. This paper presents the first-ever study of classification confidence in the context of breast mass classification.