Transformer-based out-of-distribution detection for clinically safe segmentation

In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model’s segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data.</p

Latent Transformer Models for out-of-distribution detection

Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images

HMDB 5.0: the Human Metabolome Database for 2022

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.Analytical BioScience

Association between age at disease onset of anti-neutrophil cytoplasmic antibody-associated vasculitis and clinical presentation and short-term outcomes

Objectives: ANCA-associated vasculitis (AAV) can affect all age groups. We aimed to show that differences in disease presentation and 6 month outcome between younger- A nd older-onset patients are still incompletely understood. Methods: We included patients enrolled in the Diagnostic and Classification Criteria for Primary Systemic Vasculitis (DCVAS) study between October 2010 and January 2017 with a diagnosis of AAV. We divided the population according to age at diagnosis: &lt;65 years or ≥65 years. We adjusted associations for the type of AAV and the type of ANCA (anti-MPO, anti-PR3 or negative). Results: A total of 1338 patients with AAV were included: 66% had disease onset at &lt;65 years of age [female 50%; mean age 48.4 years (s.d. 12.6)] and 34% had disease onset at ≥65 years [female 54%; mean age 73.6 years (s.d. 6)]. ANCA (MPO) positivity was more frequent in the older group (48% vs 27%; P = 0.001). Younger patients had higher rates of musculoskeletal, cutaneous and ENT manifestations compared with older patients. Systemic, neurologic,cardiovascular involvement and worsening renal function were more frequent in the older-onset group. Damage accrual, measured with the Vasculitis Damage Index (VDI), was significantly higher in older patients, 12% of whom had a 6 month VDI ≥5, compared with 7% of younger patients (P = 0.01). Older age was an independent risk factor for early death within 6 months from diagnosis [hazard ratio 2.06 (95% CI 1.07, 3.97); P = 0.03]. Conclusion: Within 6 months of diagnosis of AAV, patients &gt;65 years of age display a different pattern of organ involvement and an increased risk of significant damage and mortality compared with younger patients

Optimization of hydrogen supply chain: a case study in Malaysia

Hydrogen is regarded as the fuel of future by having greater heating value than the conventional fuels and with zero carbon emission. Most of the previous supply chain studies only consider the application of hydrogen as transportation fuel. Taking Johor as a case study, this paper aims to develop a holistic optimization model that exploits the use of hydrogen for vehicle fueling and electricity generation. Oil palm biomass and solar energy are used as the energy sources to produce hydrogen and electricity to satisfy the local energy demand. Through this study, the optimal configuration of hydrogen supply chain in Johor has been identified and the associated cost is found to be 3,644,800 USD/d

Optimization of standalone photovoltaic-based microgrid with electrical and hydrogen loads

Despite the ability of renewables to decarbonize energy use, their intermittent nature causes inconsistent energy generation, thus energy storage is required to tackle the supply-demand mismatch. While the use of hybrid battery-hydrogen energy storage for microgrids has been extensively studied, there is a lack of study on the integration of electricity and hydrogen supply systems. In other words, the concurrent targeting of hydrogen and electrical loads in a microgrid with hybrid battery-hydrogen storage is lacking. This study presents an optimization framework for the design and operation of a standalone microgrid with electrical and hydrogen loads. Two energy management strategies have been proposed and the optimization model is solved using particle swarm optimization algorithm. The proposed methodology was demonstrated through a case study and the levelized cost of energy ranges from 0.4551 USD/kWh to 0.4572 USD/kWh for the base case scenario. The optimal microgrid design in base case scenario is found to have a high value of potential energy waste possibility, indicating that the solar panel is oversized to reduce energy storage requirement. Sensitivity analysis results showed that a significant cost reduction can be achieved when only 95% of loads are targeted

A fast pathway for fear in human amygdala

A fast, subcortical pathway to the amygdala is thought to have evolved to enable rapid detection of threat. This pathway's existence is fundamental for understanding nonconscious emotional responses, but has been challenged as a result of a lack of evidence for short-latency fear-related responses in primate amygdala, including humans. We recorded human intracranial electrophysiological data and found fast amygdala responses, beginning 74-ms post-stimulus onset, to fearful, but not neutral or happy, facial expressions. These responses had considerably shorter latency than fear responses that we observed in visual cortex. Notably, fast amygdala responses were limited to low spatial frequency components of fearful faces, as predicted by magnocellular inputs to amygdala. Furthermore, fast amygdala responses were not evoked by photographs of arousing scenes, which is indicative of selective early reactivity to socially relevant visual information conveyed by fearful faces. These data therefore support the existence of a phylogenetically old subcortical pathway providing fast, but coarse, threat-related signals to human amygdala

Review of hydrogen economy in Malaysia and its way forward

Heavy fossil fuels consumption has raised concerns over the energy security and climate change while hydrogen is regarded as the fuel of future to decarbonize global energy use. Hydrogen is commonly used as feedstocks in chemical industries and has a wide range of energy applications such as vehicle fuel, boiler fuel, and energy storage. However, the development of hydrogen energy in Malaysia is sluggish despite the predefined targets in hydrogen roadmap. This paper aims to study the future directions of hydrogen economy in Malaysia considering a variety of hydrogen applications. The potential approaches for hydrogen production, storage, distribution and application in Malaysia have been reviewed and the challenges of hydrogen economy are discussed. A conceptual framework for the accomplishment of hydrogen economy has been proposed where renewable hydrogen could penetrate Malaysia market in three phases. In the first phase, the market should aim to utilize the hydrogen as feedstock for chemical industries. Once the hydrogen production side is matured in the second phase, hydrogen should be used as fuel in internal combustion engines or burners. In the final phase hydrogen should be used as fuel for automobiles (using fuel cell), fuel-cell combined heat and power (CHP) and as energy storage

Unsupervised 3D out-of-distribution detection with latent diffusion models

Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perform reconstruction-based OOD detection on 2D datasets, but do not trivially scale to 3D data. In this work, we propose to use Latent Diffusion Models (LDMs), which enable the scaling of DDPMs to high-resolution 3D medical data. We validate the proposed approach on near- and far-OOD datasets and compare it to a recently proposed, 3D-enabled approach using Latent Transformer Models (LTMs). Not only does the proposed LDM-based approach achieve statistically significant better performance, it also shows less sensitivity to the underlying latent representation, more favourable memory scaling, and produces better spatial anomaly maps. Code is available at https://github.com/marksgraham/ddpm-oodComment: Accepted at MICCAI 202