Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Lobe-wise Burden of COVID-19 Infection in Computed Tomography of Lungs using Knowledge Fusion from Multiple Datasets

Segmentation of COVID-19 infection in the lung tissue and its quantification in individual lobes is pivotal to understanding the disease’s effect. It helps to determine the disease progression and gauge the extent of medical support required. Automation of this process is challenging due to the lack of a standardized dataset with voxel-wise annotations of the lung field, lobes, and infections like ground-glass opacity (GGO) and consolidation. However, multiple datasets have been found to contain one or more classes of the required annotations. Typical deep learning-based solutions overcome such challenges by training neural networks under adversarial and multi-task constraints. We propose to train a convolutional neural network to solve the challenge while it learns from multiple data sources, each of which is annotated for only a few classes. We have experimentally verified our approach by training the model on three publicly available datasets and evaluating its ability to segment the lung field, lobes and COVID-19 infected regions. Additionally, eight scans that previously had annotations for infection and lung have been annotated for lobes. Our model quantifies infection per lobe in these scans with an average error of 4.5%.
The Impact of Interstitial Diseases Patterns on Lung CT Segmentation

Lung segmentation represents a fundamental step in the development of computer-aided decision systems for the investigation of interstitial lung diseases. In a holistic lung analysis, eliminating background areas from Computed Tomography (CT) images is essential to avoid the inclusion of noise information and spend unnecessary computational resources on non-relevant data. However, the major challenge in this segmentation task relies on the ability of the models to deal with imaging manifestations associated with severe disease. Based on U-net, a general biomedical image segmentation architecture, we proposed a light-weight and faster architecture. In this 2D approach, experiments were conducted with a combination of two publicly available databases to improve the heterogeneity of the training data. Results showed that, when compared to the original U-net, the proposed architecture maintained performance levels, achieving 0.894 ± 0.060, 4.493 ± 0.633 and 4.457 ± 0.628 for DSC, HD and HD-95 metrics, respectively, when using all patients from the ILD database for testing only, while allowing a more effficient computational usage. Quantitative and qualitative evaluations on the ability to cope with high-density lung patterns associated with severe disease were conducted, supporting the idea that more representative and diverse data is necessary to build robust and reliable segmentation tools.
Universal Transcranial Direct Current Stimulation (tDCS) Headset for targeting the bilateral Dorsolateral Prefrontal Cortex: Towards facilitating broader adoption

Electrode position affects the brain current flow intensity and distribution induced by transcranial direct current stimulation (tDCS). The dorsolateral pre-frontal cortex (DLPFC) is a common target in neuropsychology and neuropsychiatry applications. A positioning scheme and subsequently a headgear has previously been developed to target the DLPFC automatically - devoid of any scalp ruler or neuronavigation method. This approach minimizes the time cost for pre-treatment measurements without compromising targeting accuracy and induced electric field focality. The goal of this study was to further develop this headgear to facilitate broader adoption while maintaining its core design elements intact. Briefly, we developed the headset to accommodate all adult head sizes (52-62 cm) rather than having multiple sizes, to have increased robustness, enhanced visual aesthetics, and have improved usability.We recruited 8 subjects and tested the accuracy of electrode placement on various head sizes. We also tested usability with the System Usability Scale (SUS) and asked the subjects to rate visual appeal. Our study demonstrated that the newly developed headset had greater usability and was more visually appealing than its predecessor without compromising targeting accuracy.Clinical Relevance— This study introduces a headset for routine tDCS administration targeting bilateral DLPFC. The headset is highly usable, robust, and is expected to facilitate home and high-volume use.
Integrity of Steel Single Plate Shear Connections Subjected to Simulated Column Removal

Steel gravity framing systems, one of the most commonly used structural systems in the United States, have an unknown resistance to collapse when a column suffers damage that compromises its ability to carry gravity loads. One potential mechanism for these flexible systems to arrest collapse is through the development of an alternate load path in a sustained tensile configuration resulting from large vertical deflections. The ability of the system to develop such an alternate load path is partly dependent on the ability of the gravity connections to remain intact after undergoing extreme local deformations. This study experimentally evaluates the resistance of steel gravity connection subassemblages to loading consistent with the removal of an interior column. Characteristic connection behaviors are identified and peak resistance values and connection demands are reported for several different connection configurations. An approach to determine the deformations of fibers, used to discretize the connections, is also proposed that can predict fiber deformations from system displacement. Here, the approach is used to determine the fiber displacements at connection failure.
Melanoma Skin Cancer Detection Using Recent Deep Learning Models*

Melanoma is considered as one of the world’s deadly cancers. This type of skin cancer will spread to other areas of the body if not detected at an early stage. Convolutional Neural Network (CNN) based classifiers are currently considered one of the most effective melanoma detection techniques. This study presents the use of recent deep CNN approaches to detect melanoma skin cancer and investigate suspicious lesions. Tests were conducted using a set of more than 36,000 images extracted from multiple datasets. The obtained results show that the best performing deep learning approach achieves high scores with an accuracy and Area Under Curve (AUC) above 99%.
MRI Knee Domain Translation for Unsupervised Segmentation By CycleGAN (data from Osteoarthritis initiative (OAI))

Accurate quantification of bone and cartilage features is the key to efficient management of knee osteoarthritis (OA). Bone and cartilage tissues can be accurately segmented from magnetic resonance imaging (MRI) data using supervised Deep Learning (DL) methods. DL training is commonly conducted using large datasets with expert-labeled annotations. DL models perform better if distributions of testing data (target domains) are close to those of training data (source domains). However, in practice, data distributions of images from different MRI scanners and sequences are different and DL models need to re-trained on each dataset separately. We propose a domain adaptation (DA) framework using the CycleGAN model for MRI translation that would aid in unsupervised MRI data segmentation. We have validated our pipeline on five scans from the Osteoarthritis Initiative (OAI) dataset. Using this pipeline, we translated TSE Fat Suppressed MRI sequences to pseudo-DESS images. An improved MaskRCNN (IMaskRCNN) instance segmentation network trained on DESS was used to segment cartilage and femoral head regions in TSE Fat Suppressed sequences. Segmentations of the I-MaskRCNN correlated well with approximated manual segmentation obtained from nearest DESS slices (DICE = 0.76) without the need for retraining. We anticipate this technique will aid in automatic unsupervised assessment of knee MRI using commonly acquired MRI sequences and save experts’ time that would otherwise be required for manual segmentation.Clinical relevance— This technique paves the way to automatically convert one MRI sequence to its equivalent as if acquired by a different protocol or different magnet, facilitating robust, hardware-independent automated analysis. For example, routine clinically acquired knee MRI could be converted to high-resolution high-contrast images suitable for automated detection of cartilage defects
Non-convex Quadratically Constrained Quadratic Programming: Hidden Convexity, Scalable Approximation and Applications

Quadratically Constrained Quadratic Programming (QCQP) constitutes a class of computationally hard optimization problems that have a broad spectrum of applications in wireless communications, networking, signal processing, power systems, and other areas. The QCQP problem is known to be NP–hard in its general form; only in certain special cases can it be solved to global optimality in polynomial-time. Such cases are said to be convex in a hidden way, and the task of identifying them remains an active area of research. Meanwhile, relatively few methods are known to be effective for general QCQP problems. The prevailing approach of Semidefinite Relaxation (SDR) is computationally expensive, and often fails to work for general non-convex QCQP problems. Other methods based on Successive Convex Approximation (SCA) require initialization from a feasible point, which is NP-hard to compute in general. This dissertation focuses on both of the above mentioned aspects of non-convex QCQP. In the first part of this work, we consider the special case of QCQP with Toeplitz-Hermitian quadratic forms and establish that it possesses hidden convexity, which makes it possible to obtain globally optimal solutions in polynomial-time. The second part of this dissertation introduces a framework for efficiently computing feasible solutions of general quadratic feasibility problems. While an approximation framework known as Feasible Point Pursuit-Successive Convex Approximation (FPP-SCA) was recently proposed for this task, with considerable empirical success, it remains unsuitable for application on large-scale problems. This work is primarily focused on speeding and scaling up these approximation schemes to enable dealing with large-scale problems. For this purpose, we reformulate the feasibility criteria employed by FPP-SCA for minimizing constraint violations in the form of non-smooth, non-convex penalty functions. We demonstrate that by employing judicious approximation of the penalty functions, we obtain problem formulations which are well suited for the application of first-order methods (FOMs). The appeal of using FOMs lies in the fact that they are capable of efficiently exploiting various forms of problem structure while being computationally lightweight. This endows our approximation algorithms the ability to scale well with problem dimension. Specific applications in wireless communications and power grid system optimization considered to illustrate the efficacy of our FOM based approximation schemes. Our experimental results reveal the surprising effectiveness of FOMs for this class of hard optimization problems.
Federation of Brain Age Estimation in Structural Neuroimaging Data

Brain age estimation is a widely used approach to evaluate the impact of various neurological or psychiatric brain disorders on the brain developmental or aging process. Current studies show that neuroimaging data can be used to predict brain age, as it captures structural and functional changes that the brain undergoes during development and the aging process. A robust brain age prediction model not only has the potential in assisting early diagnosis of brain disorders but also helps in monitoring and evaluating effects of a treatment. Although access to large amounts of data helps build better models and validate their effectiveness, researchers often have limited access to brain data because of its challenging and expensive acquisition process. This data is not always sharable due to privacy restrictions. Decentralized models provide a way which does not require data exchange between the multiple involved groups. In this work, we propose a decentralized approach for brain age prediction and evaluate our models using features extracted from structural MRI data. Results demonstrate that our decentralized brain age model achieves similar performance compared to the models trained with all the data in one location.
On the Riemannian Search for Eigenvector Computation

Eigenvector computation is central to numerical algebra and often critical to many data analysis tasks nowadays. Most research on this problem has been focusing on projection methods like power iterations, such that this category of algorithms can achieve both optimal convergence rates and cheap per-iteration costs. In contrast, search methods belonging to another main category are less understood in this respect. In this work, we consider the leading eigenvector computation as a non-convex optimization problem on the (generalized) Stiefel manifold and covers the cases for both standard and generalized eigenvectors. It is shown that the inexact Riemannian gradient method induced by the shift-and-invert preconditioning is guaranteed to converge to one of the ground-truth eigenvectors at an optimal rate, e.g., O ( (cid:113) κ B λ 1 λ 1 − λ p +1 log 1 (cid:15) ) for a pair of real symmetric matrices ( A , B ) with B being positive deﬁnite, where λ i represents the i -th largest generalized eigenvalue of the matrix pair, p is the multiplicity of λ 1 , and κ B stands for the condition number of B . The standard eigenvector computation is recovered by setting B to an identity matrix. Our analysis reduces the dependence on the eigengap, making it the ﬁrst Riemannian eigen-solver that achieves the optimal rate. Experiments demonstrate that the proposed search method is able to deliver signiﬁcantly better performance than projection methods by taking advantages of step-size schemes.
Complexity Analysis of Resting-State and Task fMRI Using Multiscale Sample Entropy

Functional magnetic resonance imaging (fMRI) is a powerful tool that allows for analysis of neural activity via the measurement of blood-oxygenation-level-dependent (BOLD) signal. The BOLD fluctuations can exhibit different levels of complexity, depending upon the conditions under which they are measured. We examined the complexity of both resting-state and task-based fMRI using sample entropy (SampEn) as a surrogate for signal predictability. We found that within most tasks, regions of the brain that were deemed task-relevant displayed significantly low levels of SampEn, and there was a strong negative correlation between parcel entropy and amplitude.
RiNeo MR: A mixed-reality tool for newborn life support training

The first minute of life, the Golden Minute, has been defined as a critical window in which fundamental physiological processes occur for establishing spontaneous ventilation in a newborn. Resuscitation is more likely to succeed if it is performed properly and at the right time. In this scenario, simulation is an appropriate tool for training and evaluating the abilities of all staff working in the delivery room, as well as students. As simulations require a high degree of immersivity in order to be effective, the use of technologies like Virtual (VR) and mixed reality (MR) have garnered more interest in training. Currently, some VR and MR applications have been developed for adult life support training, but neonatal tools are still missing. To overcome this limitation, we present RiNeo MR, a prototype of a MR simulator for neonatal resuscitation training. The simulator consists of (i) a sensorized physical model of the newborn that allows monitoring chest compressions; (ii) a VR head mounted display that allows visualizing a virtual 3D model of the manikin and scenarios of the delivery and operating rooms. This enables students, and healthcare providers to be immersed in realistic hospital settings while performing life support procedures on the newborn manikin.Clinical Relevance—The newborn life support training (NLS) in facilities reduces term intrapartum-related deaths by 30%
Time to really share real-world data? [version 1; peer review: 2 approved, 1 approved with reservations]

Data other than that from clinical trials are important for healthcare decision making. However, unlike the vocal calls seen for more open access to trial data, there are limited efforts being made to ensure that agencies that collect real-world data (RWD) share this, despite its importance. There are many RWD sources across the world that could be readily exploited for research once shared. There are policy and privacy questions that need to be tackled, but opening up and sharing RWD offers remarkable potential for improvements in care for individuals and more effective use of limited healthcare resources. Open science should become the standard for RWD as well as clinical trials, especially those that have a high likelihood to influence practice.
A Radiomics-based Approach for Predicting Early Recurrence in Intrahepatic Cholangiocarcinoma after Surgical Resection: A Multicenter Study

This work aimed to develop a noninvasive and reliable computed tomography (CT)-based imaging biomarker to predict early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) via radiomics analysis. In this retrospective study, a total of 177 ICC patients were enrolled from three independent hospitals. Radiomic features were extracted on CT images, then 11 feature selection algorithms and 4 classifiers were to conduct a multi-strategy radiomics modeling. Six established radiomics models were selected as stable ones by robustness-based rule. Among those models, Max-Relevance Min-Redundancy (MRMR) combined with Gradient Boosting Machine (GBM) yielded the highest areas under the receiver operating characteristics curve (AUCs) of 0.802 (95% confidence interval [CI]: 0.727-0.876) and 0.781 (95% CI: 0.655-0.907) in the training and test cohorts, respectively. To evaluate the generalization of the developed radiomics model, stratification analysis was performed regarding different centers. The MRMR-GBM-based model manifested good generalization with comparable AUCs in each hospital (p > 0.05 for paired comparison). Thus, the MRMR-GBM-based model could offer a potential imaging biomarker to assist the prediction of ER in ICC in a noninvasive manner.Clinical Relevance—The proposed radiomics model achieved satisfactory accuracy and good generalization ability in predicting ER in ICC, which might assist personalized surveillance and clinical treatment strategy making.
Performance Analysis of State-Dependent M/Hk/1/L Queue with Balking

In this paper, we present an analysis for an queue with balking and state-dependent service. By using the block matrix solution, we first deduce the matrix form iterative formula of the steady-state probability. We also derive some performance measures of the system. Based on these performance analyses, we develop a cost model to determine numerically the system’s optimal cost and critical value. Finally, we perform a sensitivity analysis through numerical experiments.
ConvLSTM based Estimation Method of Incision Trajectory with Electric Knife by Connecting Restored Thermal Sources

Surgical navigation for understanding the internal structure of an organ is being actively studied, and it is necessary to estimate the incision trajectory to update the structure information dynamically. In this study, we focused on the fact that the region incised by the electric knife becomes high in temperature. Thus, we propose an estimation method of incision trajectory by restoring thermal source from diffused thermal images using a ConvLSTM and connecting the restored thermal sources. We first verified the possibility of thermal source restoration, and confirmed that the method enabled to restore the thermal source with high PSNR equivalent to 42.61. Next, we verified the accuracy of the incision trajectory from proposed method by comparing with the traditional method. The results suggested a better performance compared with the traditional method.
Development of a Virtual Stent Deployment Application to Estimate Patient-Specific Braided Stent Sizes

A virtual stent deployment application was developed to estimate the appropriate and patient-specific size of a braided stent for patients who undergo endovascular treatment for intracranial aneurysms. Comparing between the simulated deployed and the actual stents, we evaluated the accuracy of the simulation results. Our results indicated that lengths of the virtual and actual stents matched well despite the actual stent being affected by a geometrical change of the parent artery.Clinical Relevance—Surgeons need to be well-experienced to select an appropriate braided stent size for endovascular treatment of intracranial aneurysms, because the actual length of the deployed stent changes. This simulation will be helpful to make tailor-made surgical planning regardless of the surgeons’ individual skill level.
Exploring XAI for the Arts: Explaining Latent Space in Generative Music

Explainable AI has the potential to support more interactive and fluid co-creative AI systems which can creatively collaborate with people. To do this, creative AI models need to be amenable to debugging by offering eXplainable AI (XAI) features which are inspectable, understandable, and modifiable. However, currently there is very little XAI for the arts. In this work, we demonstrate how a latent variable model for music generation can be made more explainable; specifically we extend MeasureVAE which generates measures of music. We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions. We suggest that in doing so we bridge the gap between the latent space and the generated musical outcomes in a meaningful way which makes the model and its outputs more explainable and more debuggable.
Lifelong learning for text retrieval and recognition in historical handwritten document collections

Citation for published version (APA): Schomaker, L. (2021). Lifelong learning for text retrieval and recognition in historical handwritten document collections. In A. Fischer, M. Liwicki, & R. Ingold (Eds.), Handwritten Historical Document Analysis, Recognition, and Retrieval — State of the Art and Future Trends: Series in Machine Perception and Artificial Intelligence (Vol. 89, pp. 221-248). (Series in Machine Perception and Artificial Intelligence; Vol. 89). World Scientific Publishing. https://doi.org/10.1142/9789811203244_0012