7,376 research outputs found
Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features
Recognizing the phases of a laparoscopic surgery (LS) operation form its
video constitutes a fundamental step for efficient content representation,
indexing and retrieval in surgical video databases. In the literature, most
techniques focus on phase segmentation of the entire LS video using
hand-crafted visual features, instrument usage signals, and recently
convolutional neural networks (CNNs). In this paper we address the problem of
phase recognition of short video shots (10s) of the operation, without
utilizing information about the preceding/forthcoming video frames, their phase
labels or the instruments used. We investigate four state-of-the-art CNN
architectures (Alexnet, VGG19, GoogleNet, and ResNet101), for feature
extraction via transfer learning. Visual saliency was employed for selecting
the most informative region of the image as input to the CNN. Video shot
representation was based on two temporal pooling mechanisms. Most importantly,
we investigate the role of 'elapsed time' (from the beginning of the
operation), and we show that inclusion of this feature can increase performance
dramatically (69% vs. 75% mean accuracy). Finally, a long short-term memory
(LSTM) network was trained for video shot classification based on the fusion of
CNN features with 'elapsed time', increasing the accuracy to 86%. Our results
highlight the prominent role of visual saliency, long-range temporal recursion
and 'elapsed time' (a feature so far ignored), for surgical phase recognition.Comment: 6 pages, 4 figures, 6 table
A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies
The development of machine learning systems for the diagnosis of rare
diseases is challenging mainly due the lack of data to study them. Despite this
challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD)
of low-prevalence, congenital muscular dystrophies from confocal microscopy
images. The proposed CAD system relies on a Convolutional Neural Network (CNN)
which performs an independent classification for non-overlapping patches tiling
the input image, and generates an overall decision summarizing the individual
decisions for the patches on the query image. This decision scheme points to
the possibly problematic areas in the input images and provides a global
quantitative evaluation of the state of the patients, which is fundamental for
diagnosis and to monitor the efficiency of therapies.Comment: Submitted for review to Expert Systems With Application
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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