AnoFed: Adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description
In digital healthcare applications, anomaly detection is an important task to be taken into
account. For instance, in ECG (Electrocardiogram) analysis, the aim is often to detect …
account. For instance, in ECG (Electrocardiogram) analysis, the aim is often to detect …
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
An anomalous sound detection methodology for predictive maintenance
In the last decade, Anomalous Sound Detection (ASD) is becoming an increasingly
challenging task for a plethora of applications due to the widespread diffusion of Deep …
challenging task for a plethora of applications due to the widespread diffusion of Deep …
Continuous diagnosis and prognosis by controlling the update process of deep neural networks
Continuous diagnosis and prognosis are essential for critical patients. They can provide
more opportunities for timely treatment and rational allocation. Although deep-learning …
more opportunities for timely treatment and rational allocation. Although deep-learning …
Normal image guided segmentation framework for unsupervised anomaly detection
P Xing, Y Sun, D Zeng, Z Li - … on Circuits and Systems for Video …, 2023 - ieeexplore.ieee.org
Unsupervised anomaly detection is required to detect/segment anomalous samples/regions
that deviate from the normal pattern while learning only through the normal sample category …
that deviate from the normal pattern while learning only through the normal sample category …
Self-supervised guided segmentation framework for unsupervised anomaly detection
Unsupervised anomaly detection is a challenging task in industrial applications since it is
impracticable to collect sufficient anomalous samples. In this paper, a novel Self-Supervised …
impracticable to collect sufficient anomalous samples. In this paper, a novel Self-Supervised …
Learning image representations for anomaly detection: application to discovery of histological alterations in drug development
I Zingman, B Stierstorfer, C Lempp, F Heinemann - Medical Image Analysis, 2024 - Elsevier
We present a system for anomaly detection in histopathological images. In histology, normal
samples are usually abundant, whereas anomalous (pathological) cases are scarce or not …
samples are usually abundant, whereas anomalous (pathological) cases are scarce or not …
Secure and privacy-preserving federated learning with explainable artificial intelligence for smart healthcare system
A Raza - 2023 - theses.hal.science
The growing population around the globe has a significant impact on various sectors
including the labor force, healthcare, and the global economy. The healthcare sector is …
including the labor force, healthcare, and the global economy. The healthcare sector is …
On equivalence of anomaly detection algorithms
CI Jerez, J Zhang, MR Silva - ACM Transactions on Knowledge …, 2023 - dl.acm.org
In most domains, anomaly detection is typically cast as an unsupervised learning problem
because of the infeasibility of labeling large datasets. In this setup, the evaluation and …
because of the infeasibility of labeling large datasets. In this setup, the evaluation and …
Post-robustifying deep anomaly detection ensembles by model selection
Anomaly detection has been a major research area in machine learning with deep
ensemble models showing exceptional performance. However, formal verification of …
ensemble models showing exceptional performance. However, formal verification of …