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Right Heart Catheterization is a gold standard procedure for diagnosing Pulmonary Hypertension by measuring mean Pulmonary Artery Pressure (mPAP). It is invasive, costly, time-consuming and carries risks. In this paper, for the first time, we explore the estimation of mPAP from videos of noninvasive Cardiac Magnetic Resonance Imaging. To enhance the predictive capabilities of Deep Learning models used for this task, we introduce an additional modality in the form of demographic features and clinical measurements. Inspired by all-Multilayer Perceptron architectures, we present TabMixer, a novel module enabling the integration of imaging and tabular data through spatial, temporal and channel mixing. Specifically, we present the first approach that utilizes Multilayer Perceptrons to interchange tabular information with imaging features in vision models. We test TabMixer for mPAP estimation and show that it enhances the performance of Convolutional Neural Networks, 3D-MLP and Vision Transformers while being competitive with previous modules for imaging and tabular data. Our approach has the potential to improve clinical processes involving both modalities, particularly in noninvasive mPAP estimation, thus, significantly enhancing the quality of life for individuals affected by Pulmonary Hypertension. We provide a source code for using TabMixer at https://github.com/SanoScience/TabMixer., Comment: Accepted for the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2024

Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories, leveraging a zero-shot learning approach. ETHOS represents a significant advancement in foundation model development for healthcare analytics, eliminating the need for labeled data and model fine-tuning. Its ability to simulate various treatment pathways and consider patient-specific factors positions ETHOS as a tool for care optimization and addressing biases in healthcare delivery. Future developments will expand ETHOS' capabilities to incorporate a wider range of data types and data sources. Our work demonstrates a pathway toward accelerated AI development and deployment in healthcare.

Training deep neural networks for 3D segmentation tasks can be challenging, often requiring efficient and effective strategies to improve model performance. In this study, we introduce a novel approach, DeCode, that utilizes label-derived features for model conditioning to support the decoder in the reconstruction process dynamically, aiming to enhance the efficiency of the training process. DeCode focuses on improving 3D segmentation performance through the incorporation of conditioning embedding with learned numerical representation of 3D-label shape features. Specifically, we develop an approach, where conditioning is applied during the training phase to guide the network toward robust segmentation. When labels are not available during inference, our model infers the necessary conditioning embedding directly from the input data, thanks to a feed-forward network learned during the training phase. This approach is tested using synthetic data and cone-beam computed tomography (CBCT) images of teeth. For CBCT, three datasets are used: one publicly available and two in-house. Our results show that DeCode significantly outperforms traditional, unconditioned models in terms of generalization to unseen data, achieving higher accuracy at a reduced computational cost. This work represents the first of its kind to explore conditioning strategies in 3D data segmentation, offering a novel and more efficient method for leveraging annotated data. Our code, pre-trained models are publicly available at https://github.com/SanoScience/DeCode ., Comment: Accepted for the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2024

In this study, we introduce a Compton SPECT system for whole-body imaging of Actinium-225 (225Ac), one of the trending radionuclides for targeted alpha therapy (TAT). The Compton SPECT system enables multi-energy gamma photon detection with higher efficiency compared to mechanically collimated SPECT. The system consists of two detectors, providing a field of view (FOV) adequate for whole-body imaging, while achieving high sensitivity and clinically usable imaging resolution within a reasonable scanning time. This work focuses on the system design and evaluation using the Monte Carlo simulation toolkit Gate. The imaging performance is evaluated at two energy peaks (218 keV, 440 keV), representing the major detectable gamma energies generated from 225Ac. We explore the possibility of using the Compton SPECT system for treatment response monitoring in TAT. Results demonstrate an image resolution of 1.0 cm using a NEMA IQ phantom with 5.7 MBq of 225Ac simulated in a cold background. An image resolution of 1.3 cm can be achieved with a hot to background ratio of 30:1, and a resolution of 3.7 cm can be achieved with an activity ratio of 12:1. The best achievable sensitivity at 10 cm distance to the detector is 0.5% with the two energy windows (211-225 keV, 430-450 keV) selected. The proposed system may serve as an alternative imaging tool for TAT scanning in clinical settings.

During the last 25 years, hundreds of binary stars and planets have been discovered towards the Galactic Bulge by microlensing surveys. Thanks to a new generation of large-sky surveys, it is now possible to regularly detect microlensing events across the entire sky. The OMEGA Key Projet at the Las Cumbres Observatory carries out automated follow-up observations of microlensing events alerted by these surveys with the aim of identifying and characterizing exoplanets as well as stellar remnants. In this study, we present the analysis of the binary lens event Gaia20bof. By automatically requesting additional observations, the OMEGA Key Project obtained dense time coverage of an anomaly near the peak of the event, allowing characterization of the lensing system. The observed anomaly in the lightcurve is due to a binary lens. However, several models can explain the observations. Spectroscopic observations indicate that the source is located at $\le2.0$ kpc, in agreement with the parallax measurements from Gaia. While the models are currently degenerate, future observations, especially the Gaia astrometric time series as well as high-resolution imaging, will provide extra constraints to distinguish between them., Comment: Accepted in AJ

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero., Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted to IEEE Transactions on Medical Imaging (2024). Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/10628648

In this work, we present a novel, machine-learning approach for constructing Multiclass Interpretable Scoring Systems (MISS) - a fully data-driven methodology for generating single, sparse, and user-friendly scoring systems for multiclass classification problems. Scoring systems are commonly utilized as decision support models in healthcare, criminal justice, and other domains where interpretability of predictions and ease of use are crucial. Prior methods for data-driven scoring, such as SLIM (Supersparse Linear Integer Model), were limited to binary classification tasks and extensions to multiclass domains were primarily accomplished via one-versus-all-type techniques. The scores produced by our method can be easily transformed into class probabilities via the softmax function. We demonstrate techniques for dimensionality reduction and heuristics that enhance the training efficiency and decrease the optimality gap, a measure that can certify the optimality of the model. Our approach has been extensively evaluated on datasets from various domains, and the results indicate that it is competitive with other machine learning models in terms of classification performance metrics and provides well-calibrated class probabilities., Comment: Accepted for SDM24

Automatic detection and tracking of emotional states has the potential for helping individuals with various mental health conditions. While previous studies have captured physiological signals using wearable devices in laboratory settings, providing valuable insights into the relationship between physiological responses and mental states, the transfer of these findings to real-life scenarios is still in its nascent stages. Our research aims to bridge the gap between laboratory-based studies and real-life settings by leveraging consumer-grade wearables and self-report measures. We conducted a preliminary study involving 15 healthy participants to assess the efficacy of wearables in capturing user valence in real-world settings. In this paper, we present the initial analysis of the collected data, focusing primarily on the results of valence classification. Our findings demonstrate promising results in distinguishing between high and low positive valence, achieving an F1 score of 0.65. This research opens up avenues for future research in the field of mobile mental health interventions., Comment: Accepted for MobileHCI 2023

Pulmonary Hypertension (PH) is a severe disease characterized by an elevated pulmonary artery pressure. The gold standard for PH diagnosis is measurement of mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart Catheterization. In this paper, we investigate noninvasive approach to PH detection utilizing Magnetic Resonance Imaging, Computer Models and Machine Learning. We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP. We compare results of regression (with thresholding of estimated mPAP) and classification and demonstrate that metrics achieved in both experiments are comparable. The predicted mPAP values are more informative to the physicians than the probability of PH returned by classification models. They provide the intuitive explanation of the outcome of the machine learning model (clinicians are accustomed to the mPAP metric, contrary to the PH probability)., Comment: Accepted for ICCS 2022

Medical data analysis often combines both imaging and tabular data processing using machine learning algorithms. While previous studies have investigated the impact of attention mechanisms on deep learning models, few have explored integrating attention modules and tabular data. In this paper, we introduce TabAttention, a novel module that enhances the performance of Convolutional Neural Networks (CNNs) with an attention mechanism that is trained conditionally on tabular data. Specifically, we extend the Convolutional Block Attention Module to 3D by adding a Temporal Attention Module that uses multi-head self-attention to learn attention maps. Furthermore, we enhance all attention modules by integrating tabular data embeddings. Our approach is demonstrated on the fetal birth weight (FBW) estimation task, using 92 fetal abdominal ultrasound video scans and fetal biometry measurements. Our results indicate that TabAttention outperforms clinicians and existing methods that rely on tabular and/or imaging data for FBW prediction. This novel approach has the potential to improve computer-aided diagnosis in various clinical workflows where imaging and tabular data are combined. We provide a source code for integrating TabAttention in CNNs at https://github.com/SanoScience/Tab-Attention., Comment: Accepted for the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023

We report discovering an exoplanet from following up a microlensing event alerted by Gaia. The event Gaia22dkv is toward a disk source rather than the traditional bulge microlensing fields. Our primary analysis yields a Jovian planet with M_p = 0.59^{+0.15}_{-0.05} M_J at a projected orbital separation r_perp = 1.4^{+0.8}_{-0.3} AU, and the host is a ~1.1 M_sun turnoff star at ~1.3 kpc. At r'~14, the host is far brighter than any previously discovered microlensing planet host, opening up the opportunity of testing the microlensing model with radial velocity (RV) observations. RV data can be used to measure the planet's orbital period and eccentricity, and they also enable searching for inner planets of the microlensing cold Jupiter, as expected from the ''inner-outer correlation'' inferred from Kepler and RV discoveries. Furthermore, we show that Gaia astrometric microlensing will not only allow precise measurements of its angular Einstein radius theta_E, but also directly measure the microlens parallax vector and unambiguously break a geometric light-curve degeneracy, leading to definitive characterization of the lens system., Comment: Accepted by AJ

We present the results of our analysis of Gaia19dke, an extraordinary microlensing event in the Cygnus constellation that was first spotted by the {\gaia} satellite. This event featured a strong microlensing parallax effect, which resulted in multiple peaks in the light curve. We conducted extensive photometric, spectroscopic, and high-resolution imaging follow-up observations to determine the mass and the nature of the invisible lensing object. Using the Milky Way priors on density and velocity of lenses, we found that the dark lens is likely to be located at a distance of $D_L =(3.05^{+4.10}_{-2.42})$kpc, and has a mass of $M_L =(0.51^{+3.07}_{-0.40}) M_\odot$. Based on its low luminosity and mass, we propose that the lens in Gaia19dke event is an isolated white dwarf., Comment: 16 pages, submitted to A&A

This study examines the effect of COVID-19 pandemic and associated lockdowns on access to crucial diagnostic procedures for breast cancer patients, including screenings and treatments. To quantify the impact of the lockdowns on patient outcomes and cost, the study employs a mathematical model of breast cancer progression. The model includes ten different states that represent various stages of health and disease, along with the four different stages of cancer that can be diagnosed or undiagnosed. The study employs a natural history stochastic model to simulate the progression of breast cancer in patients. The model includes transition probabilities between states, estimated using both literature and empirical data. The study utilized a Markov Chain Monte Carlo simulation to model the natural history of each simulated patient over a seven-year period from 2019 to 2025. The simulation was repeated 100 times to estimate the variance in outcome variables. The study found that the COVID-19 pandemic and associated lockdowns caused a significant increase in breast cancer costs, with an average rise of 172.5 million PLN (95% CI [82.4, 262.6]) and an additional 1005 breast cancer deaths (95% CI [426, 1584]) in Poland during the simulated period. While these results are preliminary, they highlight the potential harmful impact of lockdowns on breast cancer treatment outcomes and costs., Comment: International Conference on Computational Science (ICCS) 2023, Prague

The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a traditional one. We estimate the CL experienced by other participants (13) while completing surveys. We find that CL detector performance can be enhanced via pre-training on stress detection tasks. For 10 out of 13 participants, a personalized CL detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified surveys in terms of CL but participants prefer the gamified version., Comment: Accepted for AMIA 2023

We calculate the charge and heat currents carried by electrons, originating from a temperature gradient and a chemical potential difference between the two ends of tubular nanowires with different geometries of the cross-sectional areas: circular, square, triangular, and hexagonal. We consider nanowires based on InAs semiconductor material, and use the Landauer-B\"{u}ttiker approach to calculate the transport quantities. We include impurities in the form of delta scatterers and compare their effect for different geometries. The results depend on the quantum localization of the electrons along the edges of the tubular prismatic shell. For example, the effect of impurities on the charge and heat transport is weaker in the triangular shell than in the hexagonal shell, and the thermoelectric current in the triangular case is several times larger than in the hexagonal case, for the same temperature gradient., Comment: 8 pages 8 figures

We calculate electronic charge and heat transport in tubular nanowires generated by a temperature gradient or a chemical potential bias. These nanowires correspond to semiconductor core-shell nanowires with insulating (undoped) core and conductive (doped) shell, such that the conduction takes place only in the shell. The cross section of such nanowires is typically polygonal. We study the influence of the cross section shape and shell thickness on the electric and heat conduction of the shell. We use the Landauer-B\"uttiker approach to calculate the electric and heat currents as a non-linear function of temperature and chemical potential bias beyond the linear regime., Comment: International Semiconductor Conference - CAS 2022, Romania

PolFEL stands for Polish Free Electron Laser, the first FEL research infrastructure in Poland. This facility is under development, and it will operate in three wavelength ranges: IR, THz and VUV, using different types of undulators. Machine will be driven by 200 MeV linear superconducting accelerator, which will operate in both, pulsed wave (PW) and continuous wave (CW) modes. This paper will describe the concept, current status and the first results of the LLRF systems development., Comment: Poster presented at LLRF Workshop 2022 (LLRF2022, arXiv:2208.13680)

Flux-periodic oscillations of the superconducting gap in proximitized core-shell nanowires are explored. Periodicity of oscillations in the energy spectrum of a cylindrical nanowire is compared with nanowires having hexagonal and square cross-section geometry, along with the effects of Zeeman and Rashba spin-orbit interaction. A transition between h/e and h/2e periodicity is found and shown to be dependent on the chemical potential, with correspondence to degeneracy points of the angular momentum quantum number. For a thin shell of a square nanowire, solely h/e periodicity is found in the infinite wire spectrum and shown to result from energy separation between the lowest groups of excited states., Comment: 10 pages, 11 figures, references added, improved figures

Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function. The delineations of left and right ventricle blood pools and left ventricular myocardium are important for the diagnosis of cardiac diseases. Unfortunately, the movement of a patient during the CMR acquisition procedure may result in motion artifacts appearing in the final image. Such artifacts decrease the diagnostic quality of CMR images and force redoing of the procedure. In this paper, we present a Multi-task Swin UNEt TRansformer network for simultaneous solving of two tasks in the CMRxMotion challenge: CMR segmentation and motion artifacts classification. We utilize both segmentation and classification as a multi-task learning approach which allows us to determine the diagnostic quality of CMR and generate masks at the same time. CMR images are classified into three diagnostic quality classes, whereas, all samples with non-severe motion artifacts are being segmented. Ensemble of five networks trained using 5-Fold Cross-validation achieves segmentation performance of DICE coefficient of 0.871 and classification accuracy of 0.595., Comment: Accepted for Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop, The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion) challenge