79 research outputs found
GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos
The interest in video anomaly detection systems that can detect different types of anomalies,
such as violent behaviours in surveillance videos, has gained traction in recent years. The current
approaches employ deep learning to perform anomaly detection in videos, but this approach has
multiple problems. For example, deep learning in general has issues with noise, concept drift,
explainability, and training data volumes. Additionally, anomaly detection in itself is a complex
task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly
detection using deep learning is therefore mainly constrained to generative models such as generative
adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer
from general deep learning issues and are hard to properly train. In this paper, we explore the
capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection
in videos, as it has favorable properties such as noise tolerance and online learning which combats
concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM
architecture specifically for anomaly detection in complex videos such as surveillance footage. We
have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation
results and online learning capabilities prove the great potential of using our system for real-time
unsupervised anomaly detection in complex videos
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments
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