The role of noise in denoising models for anomaly detection in medical images.

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research. Code for our DAE model and coarse noise is provided at: https://github.com/AntanasKascenas/DenoisingAE.

A framework for probabilistic weather forecast post-processing across models and lead times using machine learning.

Forecasting the weather is an increasingly data-intensive exercise. Numerical weather prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity of these models poses a new challenge for the operational meteorologist: how should the information from all available models, each with their own unique biases and limitations, be combined in order to provide stakeholders with well-calibrated probabilistic forecasts to use in decision making? In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support: probabilities of future weather outcomes. First, we use quantile regression forests to learn the error profile of each numerical model, and use these to apply empirically derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution, which we demonstrate has properties suitable for decision support. Our results suggest that this approach provides an effective and operationally viable framework for the cohesive post-processing of weather forecasts across multiple models and lead times to produce a well-calibrated probabilistic output. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

How Visuo-Spatial Mental Imagery Develops: Image Generation and Maintenance.

Two experiments examined the nature of visuo-spatial mental imagery generation and maintenance in 4-, 6-, 8-, 10-year old children and adults (N = 211). The key questions were how image generation and maintenance develop (Experiment 1) and how accurately children and adults coordinate mental and visually perceived images (Experiment 2). Experiment 1 indicated that basic image generation and maintenance abilities are present at 4 years of age but the precision with which images are generated and maintained improves particularly between 4 and 8 years. In addition to increased precision, Experiment 2 demonstrated that generated and maintained mental images become increasingly similar to visually perceived objects. Altogether, findings suggest that for simple tasks demanding image generation and maintenance, children attain adult-like precision younger than previously reported. This research also sheds new light on the ability to coordinate mental images with visual images in children and adults.

Disambiguating multi-modal scene representations using perceptual grouping constraints.

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that - in addition to commonly used geometric information - makes use of a novel multi-modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints.

A probabilistic framework for 3D visual object representation.

We present an object representation framework that encodes probabilistic spatial relations between 3D features and organizes these features in a hierarchy. Features at the bottom of the hierarchy are bound to local 3D descriptors. Higher level features recursively encode probabilistic spatial configurations of more elementary features. The hierarchy is implemented in a Markov network. Detection is carried out by a belief propagation algorithm, which infers the pose of high-level features from local evidence and reinforces local evidence from globally consistent knowledge, effectively producing a likelihood for the pose of the object in the detection scene. We also present a simple learning algorithm that autonomously builds hierarchies from local object descriptors. We explain how to use our framework to estimate the pose of a known object in an unknown scene. Experiments demonstrate the robustness of hierarchies to input noise, viewpoint changes, and occlusions.