Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201

Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?

While deep neural network models offer unmatched classification performance, they are prone to learning spurious correlations in the data. Such dependencies on confounding information can be difficult to detect using performance metrics if the test data comes from the same distribution as the training data. Interpretable ML methods such as post-hoc explanations or inherently interpretable classifiers promise to identify faulty model reasoning. However, there is mixed evidence whether many of these techniques are actually able to do so. In this paper, we propose a rigorous evaluation strategy to assess an explanation technique's ability to correctly identify spurious correlations. Using this strategy, we evaluate five post-hoc explanation techniques and one inherently interpretable method for their ability to detect three types of artificially added confounders in a chest x-ray diagnosis task. We find that the post-hoc technique SHAP, as well as the inherently interpretable Attri-Net provide the best performance and can be used to reliably identify faulty model behavior

Differential Palmit(e)oylation of Wnt1 on C93 and S224 Residues Has Overlapping and Distinct Consequences

Though the mechanisms by which cytosolic/intracellular proteins are regulated by the post-translational addition of palmitate adducts is well understood, little is known about how this lipid modification affects secreted ligands, such as Wnts. Here we use mutational analysis to show that differential modification of the two known palmit(e)oylated residues of Wnt1, C93 and S224, has both overlapping and distinct consequences. Though the relative roles of each residue are similar with respect to stability and secretion, two distinct biological assays in L cells show that modification of C93 primarily modulates signaling via a ß-catenin independent pathway while S224 is crucial for ß-catenin dependent signaling. In addition, pharmacological inhibition of Porcupine (Porcn), an upstream regulator of Wnt, by IWP1, specifically inhibited ß-catenin dependent signaling. Consistent with these observations, mapping of amino acids in peptide domains containing C93 and S224 demonstrate that acylation of C93 is likely to be Porcn-independent while that of S224 is Porcn-dependent. Cumulatively, our data strongly suggest that C93 and S224 are modified by distinct enzymes and that the differential modification of these sites has the potential to influence Wnt signaling pathway choice

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical Imagin

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation

Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals

Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models.Comment: Accepted to MIDL 202

Evaluation of Greenbug and Yellow Sugarcane Aphid Feeding Behavior on Resistant and Susceptible Switchgrass Cultivars

Switchgrass (Panicum virgatum L.) is an emerging biofuel crop that serves as host for aphids. To discern the effects of plant age and possible resistance mechanisms, the feeding behavior of greenbugs (Schizaphis graminum Rondani.) and the yellow sugarcane aphid (Sipha flava Forbes.) was monitored on three diverse switchgrasses by the electrical penetration graph (EPG) technique. Callose deposition and genes associated with callose metabolism were also analyzed to discern their association with plant resistance. There was a strong host effect on greenbugs feeding on lowland cultivar Kanlow at the V3 stage of development, as compared to the greenbug-susceptible upland cultivar Summer and plants derived from Kanlow (♂) × Summer (♀) (K×S) crosses. These data confirmed that Kanlow at the V3 stage had antibiosis to greenbugs, which was absent in the Summer and K×S plants. In contrast, similar effects were not observed for yellow sugarcane aphids, excluding significant differences in the time to first probe on Kanlow plants at the V1 stage and reduction in time spent on pathway processes on Kanlow plants at the V3 stage. These data demonstrated that Kanlow plants may have multiple sources of resistance to the two aphids, and possibly some were phloem based. Microscopy of leaf sections stained with aniline blue for callose was suggestive of increased callose deposition in the sieve elements in Kanlow plants relative to Summer and K×S plants. RT-qPCR analysis of several genes associated with callose metabolism in infested plants was equivocal. Overall, these studies suggest the presence of multiple defense mechanisms against aphids in Kanlow plants, relative to Summer and K×S plants

Clinical pharmacokinetics of antipsychotics in pediatric populations:a scoping review focusing on dosing regimen

Introduction:Achieving optimal clinical responses and minimizing side effects through precision dosing of antipsychotics in children and adolescents with psychiatric disorders remains a challenge. Identifying patient characteristics (covariates) that affect pharmacokinetics can inform more effective dosing strategies and ultimately improve patient outcomes. This review aims to provide greater insight into the impact of covariates on the clinical pharmacokinetics of antipsychotics in pediatric populations. Areas covered: A comprehensive literature search was conducted, and the main findings regarding the effects of the covariates on the pharmacokinetics of antipsychotics in children and adolescents are presented. Expert opinion: Our study highlights significant covariates, including age, sex, weight, CYP2D6 phenotype, co-medication, and smoking habits, which affect the pharmacokinetics of antipsychotics. However, the findings were generally limited by the small sample sizes of naturalistic, open-label, observational studies, and the homogeneous subgroups. Dosing based on weight and preemptive genotyping could prove beneficial for optimizing the dosing regimen in pediatric populations. Future research is needed to refine dosing recommendations and establish therapeutic reference ranges critical for precision dosing and Therapeutic Drug Monitoring (TDM). The integration of individual patient characteristics with TDM can further optimize the efficacy and safety of antipsychotics for each patient.</p

Personal care product use and lifestyle affect phthalate and DINCH metabolite levels in teenagers and young adults

Humans are widely exposed to phthalates and their novel substitutes, and considering the negative health effects associated with some phthalates, it is crucial to understand population levels and exposure determinants. This study is focused on 300 urine samples from teenagers (aged 12-17) and 300 from young adults (aged 18-37) living in Czechia collected in 2019 and 2020 to assess 17 plasticizer metabolites as biomarkers of exposure. We identified widespread phthalate exposure in the study population. The diethyl phthalate metabolite monoethyl phthalate (MEP) and three di (2-ethylhexyl) phthalate metabolites were detected in the urine of >99% of study participants. The highest median concentrations were found for metabolites of low-molecular-weight (LMW) phthalates: mono-n-butyl phthalate (MnBP), monoisobutyl phthalate (MiBP) and MEP (60.7; 52.6 and 17.6 μg/L in young adults). 1,2-cyclohexanedicarboxylic acid diisononyl ester (DINCH) metabolites were present in 68.2% of the samples with a median of 1.24 μg/L for both cohorts. Concentrations of MnBP and MiBP were similar to other European populations, but 5-6 times higher than in populations in North America. We also observed large variability in phthalate exposures within the study population, with 2-3 orders of magnitude differences in urinary metabolites between high and low exposed individuals. The concentrations varied with season, gender, age, and lifestyle factors. A relationship was found between high levels of MEP and high overall use of personal care products (PCPs). Cluster analysis suggested that phthalate exposures depend on season and multiple lifestyle factors, like time spent indoors and use of PCPs, which combine to lead to the observed widespread presence of phthalate metabolites in both study populations. Participants who spent more time indoors, particularly noticeably during colder months, had higher levels of high-molecular weight phthalate metabolites, whereas participants with higher PCP use, particularly women, tended to have higher concentration of LMW phthalate metabolites.Authors thank the Research Infrastructure RECETOX RI (No. LM2018121) and CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17_043/0009632) for a supportive background. The work was supported by the Operational Programme Research, Development and Innovation – project Cetocoen Plus (CZ.02.1.01/0.0/0.0/15_003/0000469) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 857560. This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 733032. We thank all collaborating field workers, laboratory and administrative personnel, and especially the cohort participants who invested their time and provided samples and information for this study. This study reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains.S