Deep learning for prediction of fractional flow reserve from resting coronary pressure curves.

BACKGROUND: It would be ideal for a non-hyperaemic index to predict fractional flow reserve (FFR) more accurately, given FFR's extensive validation in a multitude of clinical settings. AIMS: The aim of this study was to derive a novel non-hyperaemic algorithm based on deep learning and to validate it in an internal validation cohort against FFR. METHODS: The ARTIST study is a post hoc analysis of three previously published studies. In a derivation cohort (random 80% sample of the total cohort) a deep neural network was trained (deep learning) with paired examples of resting coronary pressure curves and their FFR values. The resulting algorithm was validated against unseen resting pressure curves from a random 20% sample of the total cohort. The primary endpoint was diagnostic accuracy of the deep learning-derived algorithms against binary FFR ≤0.8. To reduce the variance in the precision, we used a fivefold cross-validation procedure. RESULTS: A total of 1,666 patients with 1,718 coronary lesions and 2,928 coronary pressure tracings were included. The diagnostic accuracy of our convolutional neural network (CNN) and recurrent neural networks (RNN) against binary FFR ≤0.80 was 79.6±1.9% and 77.6±2.3%, respectively. There was no statistically significant difference between the accuracy of our neural networks to predict binary FFR and the most accurate non-hyperaemic pressure ratio (NHPR). CONCLUSIONS: Compared to standard derivation of resting pressure ratios, we did not find a significant improvement in FFR prediction when resting data are analysed using artificial intelligence approaches. Our findings strongly suggest that a larger class of hidden information within resting pressure traces is not the main cause of the known disagreement between resting indices and FFR. Therefore, if clinicians want to use FFR for clinical decision making, hyperaemia induction should remain the standard practice.

Evaluation of color spatio-temporal interest points for human action recognition.

This paper considers the recognition of realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena, such as shadows and highlights. In addition, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by color STIPs. Color STIPs are multichannel reformulations of STIP detectors and descriptors, for which we consider a number of chromatic and invariant representations derived from the opponent color space. Color STIPs are shown to outperform their intensity-based counterparts on the challenging UCF sports, UCF11 and UCF50 action recognition benchmarks by more than 5% on average, where most of the gain is due to the multichannel descriptors. In addition, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.

Robustifying descriptor instability using Fisher vectors.

Many computer vision applications, including image classification, matching, and retrieval use global image representations, such as the Fisher vector, to encode a set of local image patches. To describe these patches, many local descriptors have been designed to be robust against lighting changes and noise. However, local image descriptors are unstable when the underlying image signal is low. Such low-signal patches are sensitive to small image perturbations, which might come e.g., from camera noise or lighting effects. In this paper, we first quantify the relation between the signal strength of a patch and the instability of that patch, and second, we extend the standard Fisher vector framework to explicitly take the descriptor instabilities into account. In comparison to common approaches to dealing with descriptor instabilities, our results show that modeling local descriptor instability is beneficial for object matching, image retrieval, and classification.