Oct 1, 2023 · This approach consists of training generative models on healthy data, and defining anomalies as deviations from the defined model of normality ...

This paper presents a deep learning approach for abnormality detection in full-body PET scans using a VQVAE+Transformer network. The proposed method ...

We propose a new spatial conditioning mechanism that enables models to adapt and learn from varying data geometries, and apply it to a state-of-the-art Vector- ...

Oct 8, 2023 · We propose a new spatial conditioning mechanism that enables models to adapt and learn from varying data geometries, and apply it to a state-of-the-art Vector- ...

Sep 21, 2024 · We propose a new spatial conditioning mechanism that enables models to adapt and learn from varying data geometries, and apply it to a state-of- ...

Aug 3, 2020 · Here, we present a systematic framework for detecting interfaces and related anomalies in data which may fail to satisfy the manifold hypothesis.

We consider the problem of anomaly detection in images, and present a new detection technique. Given a sample of images, all known to belong to a “normal”.

Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking ... Geometry-Invariant Abnormality Detection. A Patel, PD ...

Here we propose a novel isometry invariant shape descriptor for brain morphometry analysis. First, we calculate a global area-preserving mapping from cortical ...

In this paper, we propose the TIR measure algorithm based on Riemannian geometry and apply this algorithm to abnormality detection tasks.