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

Non-Engine Order Blade Vibration in a High Pressure Compressor

International audienceHigh amplitude levels of blade vibration have occurred on the first rotor of a multi stage high pressure compressor. The frequencies are not in resonance with harmonics of the rotor speed. The excitation is aerodynamically caused and associated with a rotating flow instability in the blade tip region of the first compressor stage. A vortex shedding mechanism can be interpreted as a rotating source to generate pressure waves. The source moves relative to the blade row at a fraction of the rotor speed, similar to the 'well known mechanism' of rotating stall. To investigate the unsteady flow field in the tip region of the rotor and its relation to the blade vibration, measurements of the pressure and velocity fluctuations in the vicinity of the blade tips are compared with blade vibrations. A calculation model of the spectral characteristics of the pressure fluctuations confirms the measured data. The effect is sensitive to aerodynamic blade loading so that a modification in the design could reduce the mechanism of the rotating excitation

Measuring cosmological bulk flows via the kinematic Sunyaev-Zeldovich effect in the upcoming cosmic microwave background maps

We propose a new method to measure the possible large-scale bulk flows in the Universe from the cosmic microwave background (CMB) maps from the upcoming missions, MAP and Planck. This can be done by studying the statistical properties of the CMB temperature field at many X-ray cluster positions. At each cluster position, the CMB temperature fluctuation will be a combination of the Sunyaev-Zeldovich (SZ) kinematic and thermal components, the cosmological fluctuations and the instrument noise term. When averaged over many such clusters the last three will integrate down, whereas the first one will be dominated by a possible bulk flow component. In particular, we propose to use all-sky X-ray cluster catalogs that should (or could) be available soon from X-ray satellites, and then to evaluate the dipole component of the CMB field at the cluster positions. We show that for the MAP and Planck mission parameters the dominant contributions to the dipole will be from the terms due to the SZ kinematic effect produced by the bulk flow (the signal we seek) and the instrument noise (the noise in our signal). Computing then the expected signal-to-noise ratio for such measurement, we get that at the 95 % confidence level the bulk flows on scales >100h^{-1} Mpc can be probed down to the amplitude of $< 200$ km/sec with the MAP data and down to only 30 km/sec with the Planck mission.Comment: Astrophysical Journal Letters, in pres

Scaling of polymers in aligned rods

We study the behavior of self avoiding polymers in a background of vertically aligned rods that are either frozen into random positions or free to move horizontally. We find that in both cases the polymer chains are highly elongated, with vertical and horizontal size exponents that differ by a factor of 3. Though these results are different than previous predictions, our results are confirmed by detailed computer simulations.Comment: 4 pages, 4 figure

Gene identification for the cblD defect of vitamin B12 metabolism

Background Vitamin B12 (cobalamin) is an essential cofactor in several metabolic pathways. Intracellular conversion of cobalamin to its two coenzymes, adenosylcobalamin in mitochondria and methylcobalamin in the cytoplasm, is necessary for the homeostasis of methylmalonic acid and homocysteine. Nine defects of intracellular cobalamin metabolism have been defined by means of somatic complementation analysis. One of these defects, the cblD defect, can cause isolated methylmalonic aciduria, isolated homocystinuria, or both. Affected persons present with multisystem clinical abnormalities, including developmental, hematologic, neurologic, and metabolic findings. The gene responsible for the cblD defect has not been identified. Methods We studied seven patients with the cblD defect, and skin fibroblasts from each were investigated in cell culture. Microcell-mediated chromosome transfer and refined genetic mapping were used to localize the responsible gene. This gene was transfected into cblD fibroblasts to test for the rescue of adenosylcobalamin and methylcobalamin synthesis. Results The cblD gene was localized to human chromosome 2q23.2, and a candidate gene, designated MMADHC (methylmalonic aciduria, cblD type, and homocystinuria), was identified in this region. Transfection of wild-type MMADHC rescued the cellular phenotype, and the functional importance of mutant alleles was shown by means of transfection with mutant constructs. The predicted MMADHC protein has sequence homology with a bacterial ATP-binding cassette transporter and contains a putative cobalamin binding motif and a putative mitochondrial targeting sequence. Conclusions Mutations in a gene we designated MMADHC are responsible for the cblD defect in vitamin B12 metabolism. Various mutations are associated with each of the three biochemical phenotypes of the disorder

Comparison of organic apricot production under all-season rain protection, seasonal rain protection and without rain protection

Comparison of organic apricot production under all-season rain protection, seasonal rain protection and without rain protection

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

Fine-scale spatial and temporal acoustic occurrence of island-associated odontocetes near a mid-oceanic atoll in the northern Indian Ocean

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Panicker, D., Baumgartner, M. F., & Stafford, K. M. Fine-scale spatial and temporal acoustic occurrence of island-associated odontocetes near a mid-oceanic atoll in the northern Indian Ocean. Marine Ecology Progress Series, 683, (2022): 195–208, https://doi.org/10.3354/meps13947.Temporal patterns of oceanic predators can provide valuable information on both lunar and diel influences not just on the distributions of these predators, but also on prey patches that are often difficult to study. Mid-oceanic island chains in the northern Indian Ocean have high odontocete occurrence, but the ecology of these animals is not well characterized. We investigated fine-scale spatial and temporal patterns of island-associated odontocetes using passive acoustic monitoring from January 2019 to January 2020 around Kavaratti Island, Lakshadweep, India. Based on opportunistic recordings in the presence of odontocetes, the majority of the detected whistles were likely made by spinner dolphins Stenella longirostris. We identified a resident population whose whistle occurrence was significantly influenced by month, site, and diel and lunar cycles. More acoustic detections were made in the northeast monsoon month of November and fewer during pre-monsoon and southwest monsoon periods. Distinct day-night differences along with fine-scale temporal variability were also observed, suggesting that delphinids use nearshore waters as a daytime resting habitat. Odontocete detections were highest during the new moon period and lowest during the first quarter phase. Detection rates were higher on the south side of the island. Our study shows that solar and lunar cycles modulate odontocete vocal occurrence, presumably through influences on their prey. Similarities of odontocete occurrence around Lakshadweep to other mid-oceanic island chains suggests that an island-associated micronekton community may exist around Lakshadweep that may also be important to other pelagic species targeted by local fisheries.Funding was provided by the Office of Naval Research Marine Mammal Biology Program, USA, under grant N000141812795. We thank Ajith Kumar, the National Centre for Biological Sciences and Idrees Babu for in-country support

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