Reviewerly/exHarmony huggingface dataset:
Evidence, computation and AI: why evidence is not just in the head

Can scientific evidence outstretch what scientists have mentally entertained, or could ever entertain? This article focuses on the plausibility and consequences of an affirmative answer in a special case. Specifically, it discusses how we may treat automated scientific data-gathering systems—especially AI systems used to make predictions or to generate novel theories—from the point of view of confirmation theory. It uses AlphaFold2 as a case study.
How scene encoding affects memory discrimination: Analysing eye movements data using data driven methods

Encoding of visual scenes remains under-explored due to methodological limitations. In this study, we evaluated the relationship between memory accuracy for visual scenes and eye movements at encoding. First, we used data-driven methods, a fixation density map (using iMap4) and a saliency map (using GBVS), to analyse the visual attention for items. Second, and in a more novel way, we conducted scanpath analyses without a priori (using ScanMatch). Scene memory accuracy was assessed by asking participants to discriminate identical scenes (targets) among rearranged scenes sharing some items with targets (distractors) and new scenes. Shorter fixation duration in regions of interest (ROIs) at encoding was associated with a better rejection of distractors; there was no significant difference in the relative fixation time in ROIs at encoding, between subsequent hits and misses at test. Hence, density of eye fixations in data-driven ROIs seems to be a marker of subsequent memory discrimination and pattern separation. Interestingly, we also identified a negative correlation between average MultiDimensional Scaling (MDS) distance scanpaths and the correct rejection of distractors, indicating that scanpath consistency significantly affects the ability to discriminate distractors from targets. These data suggest that visual exploration at encoding participates in discrimination processes at test.
The Wealth and Poverty of African States: Economic Growth, Living Standards and Taxation since the Late Nineteenth Century

"The Wealth and Poverty of African States: Economic Growth, Living Standards and Taxation since the Late Nineteenth Century." The Journal of Development Studies, ahead-of-print(ahead-of-print), pp. 1–3
Combining arterial and venous CT scans in a multi-encoder network for improved hepatic vessel segmentation

The segmentation of hepatic vessel systems is important for many applications treating malignant liver disease such as selective internal radiation therapy. To enhance the different vessel systems, contrast agent is injected intravenously and CT scans are obtained at different time points and phases of contrast agent perfusion in liver vessels and parenchyma. We demonstrate that using the registered arterial phase as additional input for training an aU-Net for portal vein (PV) and hepatic vein (HV) segmentation increases the centerline Dice. However, using the registered venous phase for hepatic artery (HA) segmentation does not improve segmentation performance.
3D-2D GAN based brain metastasis synthesis with configurable parameters for fully 3D data augmentation

Recent technological advances in deep learning (DL) have led to more accurate brain metastasis (BM) detection. As a data driven approach, DL’s performance highly relies on the size and quality of the training data. However, collecting large amount of medical data is costly, and it’s difficult to include BMs with various locations, sizes, and structures etc. Thus, we propose a 3D-2D GAN for fully 3D BM synthesis with configurable parameters. First, two 3D networks are used to synthesize the mask and quantized intensity map of a lesion from 3 concentric spheres, which are used to control the lesion’s location, size and structure. Then, a 2D network is used to synthesize the final lesion with proper appearance from the quantized intensity map and the background MR image. With this 3D-2D design, the 3D networks enable the synthetic metastasis to be spatially continuous in all 3 dimensions through the guidance of the 3D intermediate presentation of the lesion, while the 2D network enables the use of 2D perceptual loss to make the final synthesized lesion look realistic. In addition, different network up-sampling strategies and postprocessing are used to control the heterogeneity and contrast of the synthetic lesion. All the synthesized images were reviewed by a radiologist. The indistinguishability rate of the synthesized lesion is above 70%. The configurable parameters for the lesion’s location, size, and structure, heterogeneity and contrast were reviewed to be effective. Our work demonstrates the feasibility of synthesizing configurable 3D BM lesions for fully 3D data augmentation.
Analyzing components of a transformer under different dataset scales in 3D prostate CT segmentation

Literature on medical imaging segmentation claims that hybrid UNet models containing both Transformer and convolutional blocks perform better than purely convolutional UNet models.This recently touted success of hybrid Transformers warrants an investigation into which of its components contribute to its performance.Also, previous work has a limitation of analysis only at fixed dataset scales as well as unfair comparisons with other models where parameter counts are not equivalent.Here, we investigate the performance of a hybrid Transformer network i.e. the nnFormer for organ segmentation in prostate CT scans.We do this in context of replacing its various components and by constructing learning curves by plotting model performance at different dataset scales.To compare with literature, the first experiment replaces all the shifted-window(swin) Transformer blocks of the nnFormer with convolutions.Results show that the convolution prevails as the data scale increases.In the second experiment, to reduce complexity, the self-attention mechanism within the swin-Transformer block is replaced with an similar albeit simpler spatial mixing operation i.e. max-pooling.We observe improved performance for max-pooling in smaller dataset scales, indicating that the window-based Transformer may not be the best choice in both small and larger dataset scales.Finally, since convolution has an inherent local inductive bias of positional information, we conduct a third experiment to imbibe such a property to the Transformer by exploring two kinds of positional encodings.The results show that there are insignificant improvements after adding positional encoding, indicating the hybrid swin-Transformers deficiency in capturing positional information given our dataset at its various scales.Through this work, we hope to motivate the community to use learning curves under fair experimental settings to evaluate the efficacy of newer architectures like Transformers for their medical imaging tasks.Code is available on https://github.com/prerakmody/window-transformer-prostate-segmentation.
Characteristics and generative mechanisms of software development productivity distributions

There is considerable variation in the productivity of software developers. Better knowledge about this variation may provide valuable inputs for the design of skill tests and recruitment processes. This paper aims to identify properties of software development productivity distributions and gain insight into mechanisms that potentially explain these productivity differences. Four data sets that contain the results of software developers solving the same programming tasks were collected. The properties of the productivity distributions were analyzed, the fits of different types of distributions to the productivity data were compared, and potential generative mechanisms that would lead to the types of distributions with the best fit to the productivity data were evaluated. The coefficient of variance of the productivity of the software developers was, on average, 0.55, with the top 50% of developers having average productivity that was 2.44 times higher than the bottom 50% of developers. All productivity samples were right-skewed, with an average skew of 1.79. About 30% of the observed productivity variance was explained by non-systematic, i.e., within-developer, variance. The distributions with the best fit to the empirical productivity data were the lognormal and power-law-with-an-exponential-cutoff distributions. The analysis of the mechanisms leading to productivity differences found no support for the "rich-getting-richer" explanation proposed for other disciplines. Instead, it suggests a constant productivity difference with increasing experience. The substantial difference in productivity among software developers solving programming tasks indicates that a thorough evaluation of skill in the recruitment process can be rewarding. In particular, the long tail towards higher productivity values demonstrates the large gains that can be achieved by detecting and recruiting developers with very high productivity. More research is needed to understand the mechanisms leading to the large productivity differences.
Deep curriculum learning for follow-up MRI registration in glioblastoma

This paper presents a weakly supervised deep convolutional neural network-based approach to perform voxel-level 3D registration between subsequent follow-up MRI scans of the same patient. To handle the large deformation in the surrounding brain tissues due to the tumor’s mass effect we proposed curriculum learning-based training for the network. Weak supervision helps the network to concentrate more focus on the tumor region and resection cavity through a saliency detection network. Qualitative and quantitative experimental results show the proposed registration network outperformed two popular state-of-the-art methods.
Multi-step reinforcement learning for medical image super-resolution

We apply a multi-agent Reinforcement Learning (RL) algorithm to single image super-resolution (SISR). In our novel implementation, each agent chooses a particular action from a fixed action set comprised of existing local enhancement operators to update each pixel intensity value. The pixel-wise arrangement of agents enables the algorithm to increase the resolution of an image by choosing optimal pixel intensity values from each option in a content-aware manner. While previous implementations of the model on SISR use Generative Adversarial Network (GAN)-based algorithms, we demonstrate that local operators can produce promising improved results without relying on the additional overhead of using machine learning techniques for the action space. Notably, we apply the proposed method to medical images, whereas previous implementations focused on natural images.
The lifecycle of Technical Debt that manifests in both source code and issue trackers

Context: Although Technical Debt (TD) has increasingly gained attention in recent years, most studies exploring TD are based on a single source (e.g., source code, code comments or issue trackers).Objective: Investigating information combined from different sources may yield insight that is more than the sum of its parts.In particular, we argue that exploring how TD items are managed in both issue trackers and software repositories (including source code and commit messages) can shed some light on what happens between the commits that incur TD and those that pay it back.Method: To this end, we randomly selected 3,000 issues from the trackers of five projects, manually analyzed 300 issues that contained TD information, and identified and investigated the lifecycle of 312 TD items. Results:The results indicate that most of the TD items marked as resolved in issue trackers are also paid back in source code, although many are not discussed after being identified in the issue tracker.Test Debt items are the least likely to be paid back in source code.We also learned that although TD items may be resolved a few days after being identified, it often takes a long time to be identified (around one year).In general, time is reduced if the same developer is involved in consecutive moments (i.e., introduction, identification, repayment decision-making and remediation), but whether the developer who paid back the item is involved in discussing the TD item does not seem to affect how quickly it is resolved.Conclusions: Investigating how developers manage TD across both source code repositories and issue trackers can lead to a more comprehensive oversight of this activity and support efforts to shorten the lifecycle of undesirable debt.
Synthesis of pediatric brain tumor images with mass effect

In children, brain tumors are the leading cause of cancer-related death. The amount of labeled data in children is much lower than that for adult subjects. This paper proposes a new method to synthesize high-quality pathological pediatric MRI brain images from pathological adult ones. To realistically simulate the appearance of brain tumors, the proposed method considers the mass effect, i.e., the deformation induced by the tumor to the surrounding tissue. First, a probabilistic U-Net was trained to predict a deformation field that encodes the mass effect from the healthy-pathological image pair. Second, the learned deformation field was utilized to warp the healthy mask to simulate the mass effect. The tumor mask is also added to the warped mask. Finally, a label-to-image transformer, i.e., the SPADE GAN, was trained to synthesize a pathological image from the segmentation masks of gray matter, white matter, CSF and the tumor. The synthetic images were evaluated in two quantitative ways: i) three supervised segmentation pipelines were trained on datasets with and without synthetic images. Two pipelines show over 1% improvements in the Dice scores when the datasets were augmented with synthetic data. ii) The Fr´echet inception distance was measured between real and synthetic image distributions. Results show that SPADE outperforms the state-of-the-art Pix2PixHD method in both T1w and T2w modalities. The source code can be accessed on https://github.com/audreyeternal/pediatric-tumor-generation
Reputation based novel trust management framework with enhanced availability for cloud

To select a cloud service provider (CSP) out of immense number of relevant CSPs available, cloud customer (CC) trusts on the reviews provided by the CCs who have used the services provided by CSPs. However, relying on these feedbacks may be hazardous if the review(s) have been deliberately posted with wicked motive. To mitigate the impact of malicious feedbacks, a framework, namely highly available credible cloud trust management framework (HA2CTMF) has been proposed with the intent of revamping the reputation and availability parameters of QoS by evaluating the trust of cloud service providers (CSPs) based on the credibility of feedbacks furnished by CC. HA2CTMF detects collusion and Sybil attacks on the labeled CloudArmor dataset and increases availability of the trust service. Collusion attack has been detected by comparing review count; service count; & mean of reviews with their respective threshold values. Further, Sybil attack has been uncovered with the help of these aspects: deviation from mean of reviews; pattern detection; & comparison of polarity value from both lower & upper threshold values. Additionally, enhanced availability has been ensured by working on disaster management and diminishing repercussions of single point of failure. The results have been accessed on the following parameters: precision; recall; f1-score & availability percentage on the dataset comprising 8774 data points. This framework has exhibited precision of 85.69%, recall of 89.45%, f1-score of 87.53% and availability of 96.71% compared to CloudArmor framework values on the same parameters as: 44.47%; 62.21%; 51.85% & 70.43% respectively. As per the results attained, we conclude that proposed HA2CTMF has demonstrated better outcome.
Towards lifting the trade-off between accuracy and adversarial robustness of deep neural networks with application on COVID 19 CT image classification and medical image segmentation

Deep neural networks (DNNs) are vulnerable to adversarial noise. Adversarial training is a general strategy to improve DNN robustness. But training a DNN model with adversarial noises may result in a much lower accuracy on clean data. Towards lifting this trade-off, we propose an adversarial training method that generates optimal adversarial training samples. Our method is evaluated on four public medical datasets, with popular deep learning models on image classification and segmentation tasks. The results show that our method has the best robustness against adversarial noises and has a minimal accuracy degradation compared to the other defense methods.
Skeletal modeling of the tricuspid valve via cylindrical parameterization

Hypoplastic left heart syndrome is a severe congenital heart defect requiring surgical intervention shortly after birth. The surgery is complex and often leads to complications requiring additional surgeries. Understanding the relationship between the structure of the tricuspid valve and functional complications could lead to more powerful diagnostic and treatment options. Because the tricuspid valve does not have spherical topology, many traditional methods for creating boundary-based or skeleton-based shape models that require spherical parameterization of an object are not applicable unless individual leaflets are independently parameterized and then merged in a multi-object model. Instead we propose to create skeletal models (s-reps) of the entire tricuspid valve structure using a cylindrical parameterization. We modify a traditional cylindrical parameterization approach by adaptively changing angle sampling based on landmarks to produce anatomically relevant correspondence across a population of objects. From this we derive s-reps which yield an improved shape space and classification performance compared with previous approaches.
Self-supervised learning enhanced ultrasound video thyroid nodule tracking

Thyroid nodules are found in 19% to 67% of individuals who are screened for thyroid cancer using ultrasonography. The large number of individuals examined for thyroid cancer is placing significant stress on radiologists and the healthcare system. Video-scale detection requires more training data than frame-scale detection. To alleviate the need for large-scale labeled datasets, a patch-scale self-supervised pre-training model was trained on unlabeled data to extract patch-distinguishing features, which are crucial for object detection. The pre-trained model was transferred to the video-based model to improve the performance of nodule detection. Experimental test results on 22 ultrasound videos containing 47 nodules show that the performance of our proposed method is 0.523 for mAP@50 and 0.430 for HOTA. The proposed method can process at 23 fps, which can meet the requirements of real-time tracking in screening scenarios.
Synthesizing audio from tongue motion during speech using tagged MRI via transformer

Investigating the relationship between internal tissue point motion of the tongue and oropharyngeal muscle deformation measured from tagged MRI and intelligible speech can aid in advancing speech motor control theories and developing novel treatment methods for speech related-disorders. However, elucidating the relationship between these two sources of information is challenging, due in part to the disparity in data structure between spatiotemporal motion fields (i.e., 4D motion fields) and one-dimensional audio waveforms. In this work, we present an efficient encoder-decoder translation network for exploring the predictive information inherent in 4D motion fields via 2D spectrograms as a surrogate of the audio data. Specifically, our encoder is based on 3D convolutional spatial modeling and transformer-based temporal modeling. The extracted features are processed by an asymmetric 2D convolution decoder to generate spectrograms that correspond to 4D motion fields. Furthermore, we incorporate a generative adversarial training approach into our framework to further improve synthesis quality on our generated spectrograms. We experiment on 63 paired motion field sequences and speech waveforms, demonstrating that our framework enables the generation of clear audio waveforms from a sequence of motion fields. Thus, our framework has the potential to improve our understanding of the relationship between these two modalities and inform the development of treatments for speech disorders.
Anatomy-specific acquisition-agnostic affine registration learned from fictitious images

Affine image registration is a cornerstone of medical image processing backed by decades of development. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every new image pair. In contrast, deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but these methods tend to be susceptible to domain shift. A network trained on a specific image type may perform poorly if an image characteristic changes at test time, such as the imaging contrast or resolution. Secondly, many classical and DL registration algorithms cannot distinguish between relevant and irrelevant anatomy: the global nature of the linear registration problem means that accuracy will suffer if parts of the image deform independently. This is why neuroimage processing, for example, often starts with brain extraction, to enhance the accuracy of brain-specific registration. We address these shortcomings of linear registration by training deep neural networks using a generative strategy that synthesizes wildly varying images from label maps. Optimizing label overlap decouples the loss from the image appearance, encouraging network invariance to acquisition specifics. It also enables the registration model to distinguish between anatomy of interest and irrelevant structures, which alleviates the need for segmenting images prior to registration to remove distracting content. We test brain-specific registration across a variety of magnetic resonance imaging protocols that approximate the diversity of real-world data, demonstrating consistent and improved accuracy relative to state-of-the-art baselines. We freely distribute our easy-to-use tool at https://w3id.org/synthmorph.
Pseudo-contrast cardiac CT angiography derived from non-contrast CT using conditional generative adversarial networks

Contrast computed tomography angiography (CTA) is utilized in wide variety of applications ranging from clinical practices to emerging technologies. However, radiation exposure, the necessity of contrast administration, as well as the overall complexity of the acquisition are major limitations. We aimed to generate pseudo-contrast CTA, utilizing a conditional generative adversarial network (cGAN). We synthesize realistic contrast CTA from a perfectly registered non-contrast thin slice computed tomography (NCCT). Our method may substitute contrast CTA with a pseudo-contrast CTA for certain clinical applications such as the assessments of cardiac anatomy.