101 research outputs found
Deep Learning based Novel Anomaly Detection Methods for Diabetic Retinopathy Screening
Programa Oficial de Doutoramento en Computación. 5009V01[Abstract] Computer-Aided Screening (CAS) systems are getting popularity in disease diagnosis. Modern CAS systems exploit data driven machine learning algorithms including supervised and unsupervised methods.
In medical imaging, annotating pathological samples are much harder and time consuming work than healthy samples. Therefore, there is always an abundance of healthy samples and scarcity of annotated and labelled pathological samples. Unsupervised anomaly detection algorithms
can be implemented for the development of CAS system using the largely available healthy samples, especially when disease/nodisease decision is important for screening.
This thesis proposes unsupervised machine learning methodologies for anomaly detection in retinal fundus images. A novel patchbased image reconstructor architecture for DR detection is presented, that addresses the shortcomings of standard autoencoders-based reconstructors.
Furthermore, a full-size image based anomaly map generation methodology is presented, where the potential DR lesions can be visualized at the pixel-level. Afterwards, a novel methodology is proposed to extend the patch-based architecture to a fully-convolutional
architecture for one-shot full-size image reconstruction. Finally, a novel methodology for supervised DR classification is proposed that utilizes the anomaly maps
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
Fine-grained and instance-level recognition methods are commonly trained and
evaluated on specific domains, in a model per domain scenario. Such an
approach, however, is impractical in real large-scale applications. In this
work, we address the problem of universal image embedding, where a single
universal model is trained and used in multiple domains. First, we leverage
existing domain-specific datasets to carefully construct a new large-scale
public benchmark for the evaluation of universal image embeddings, with 241k
query images, 1.4M index images and 2.8M training images across 8 different
domains and 349k classes. We define suitable metrics, training and evaluation
protocols to foster future research in this area. Second, we provide a
comprehensive experimental evaluation on the new dataset, demonstrating that
existing approaches and simplistic extensions lead to worse performance than an
assembly of models trained for each domain separately. Finally, we conducted a
public research competition on this topic, leveraging industrial datasets,
which attracted the participation of more than 1k teams worldwide. This
exercise generated many interesting research ideas and findings which we
present in detail. Project webpage: https://cmp.felk.cvut.cz/univ_emb/Comment: ICCV 2023 Accepte
Violence detection based on spatio-temporal feature and fisher vector
© Springer Nature Switzerland AG 2018. A novel framework based on local spatio-temporal features and a Bag-of-Words (BoW) model is proposed for violence detection. The framework utilizes Dense Trajectories (DT) and MPEG flow video descriptor (MF) as feature descriptors and employs Fisher Vector (FV) in feature coding. DT and MF algorithms are more descriptive and robust, because they are combinations of various feature descriptors, which describe trajectory shape, appearance, motion and motion boundary, respectively. FV is applied to transform low level features to high level features. FV method preserves much information, because not only the affiliations of descriptors are found in the codebook, but also the first and second order statistics are used to represent videos. Some tricks, that PCA, K-means++ and codebook size, are used to improve the final performance of video classification. In comprehensive consideration of accuracy, speed and application scenarios, the proposed method for violence detection is analysed. Experimental results show that the proposed approach outperforms the state-of-the-art approaches for violence detection in both crowd scenes and non-crowd scenes
Median K-flats for hybrid linear modeling with many outliers
We describe the Median K-Flats (MKF) algorithm, a simple online method for
hybrid linear modeling, i.e., for approximating data by a mixture of flats.
This algorithm simultaneously partitions the data into clusters while finding
their corresponding best approximating l1 d-flats, so that the cumulative l1
error is minimized. The current implementation restricts d-flats to be
d-dimensional linear subspaces. It requires a negligible amount of storage, and
its complexity, when modeling data consisting of N points in D-dimensional
Euclidean space with K d-dimensional linear subspaces, is of order O(n K d D+n
d^2 D), where n is the number of iterations required for convergence
(empirically on the order of 10^4). Since it is an online algorithm, data can
be supplied to it incrementally and it can incrementally produce the
corresponding output. The performance of the algorithm is carefully evaluated
using synthetic and real data
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