Estimation of the Impact of COVID-19 Pandemic Lockdowns on Breast Cancer Deaths and Costs in Poland using Markovian Monte Carlo Simulation

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, Pragu

Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?

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 202

TabAttention: Learning Attention Conditionally on Tabular Data

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) 202

Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results

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. Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/1062864

Improving the discovery of technological opportunities using patent classification based on explainable neural networks

Purpose: The paper aims to present an approach supporting the improvement of technological opportunities discovery using patent classification based on explainable neural networks. Design/Methodology/Approach: Empirical research was conducted applying a dataset containing U.S. patent documents. Firstly, this dataset was checked for the correctness of the saved patent data to be further analyzed. Then, a custom Bidirectional Encoder Representations from Transformers (BERT) Neural Network was developed and trained. Finally, the Local Interpretable Model-agnostic Explanations (LIME) method was applied for interpreting the results achieved with the BERT classifier. Findings: The studied classifier achieved high quality (precision of 80.6%), allowing correct classification of the technologies described in the patents. Such neural classifiers are easy to use in practice and highly versatile; however, there is an insufficient trust of managers in the decisions suggested by that black-box method. The proposed new approach may help overcome the lack of trust of the users of neural models towards the technological opportunities suggested by them. Practical Implications: Various patent databases are often used to discover innovative solutions, as well as economic and technological opportunities, because they contain vast resources of prosperous and extensive information recorded in patent documentation. Such analyzes are critical to businesses and public organizations as they help them make decisions about carrying out strategic investment projects. The presented approach, which supports the improvement of automated processes of technological opportunities discovery, may increase confidence in the results obtained using neural classifiers. Originality/Value: Earlier studies focused mainly on using more effective classifiers and better learning algorithms. Progress in this type of research did not help solve the problem of the lack of reliable justification for individual decisions indicated by machine learning models. In this study, a proposal for an approach enables the discovery of technological opportunities using patent classification based on explainable neural networks.peer-reviewe

Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models

There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of clear, readable and trustworthy justification of STLF obtained using such models is a serious problem that needs to be tackled. The article proposes an approach based on the local interpretable model-agnostic explanations (LIME) method that supports reliable premises justifying and explaining the forecasts. The use of the proposed approach makes it possible to improve the reliability of heuristic and experimental neural modeling processes, the results of which are difficult to interpret. Explaining the forecasting may facilitate the justification of the selection and the improvement of neural models for STLF, while contributing to a better understanding of the obtained results and broadening the knowledge and experience supporting the enhancement of energy systems security based on reliable forecasts and simplifying dispatch decisions

Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models

There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of clear, readable and trustworthy justification of STLF obtained using such models is a serious problem that needs to be tackled. The article proposes an approach based on the local interpretable model-agnostic explanations (LIME) method that supports reliable premises justifying and explaining the forecasts. The use of the proposed approach makes it possible to improve the reliability of heuristic and experimental neural modeling processes, the results of which are difficult to interpret. Explaining the forecasting may facilitate the justification of the selection and the improvement of neural models for STLF, while contributing to a better understanding of the obtained results and broadening the knowledge and experience supporting the enhancement of energy systems security based on reliable forecasts and simplifying dispatch decisions

BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video

Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of weight using prenatal ultrasound is challenging as it requires images of specific fetal body parts during advanced pregnancy which is difficult to capture due to poor quality of images caused by the lack of amniotic fluid. As a consequence, predictions which rely on standard methods often suffer from significant errors. In this paper we propose the Residual Transformer Module which extends a 3D ResNet-based network for analysis of 2D+t spatio-temporal ultrasound video scans. Our end-to-end method, called BabyNet, automatically predicts fetal birth weight based on fetal ultrasound video scans. We evaluate BabyNet using a dedicated clinical set comprising 225 2D fetal ultrasound videos of pregnancies from 75 patients performed one day prior to delivery. Experimental results show that BabyNet outperforms several state-of-the-art methods and estimates the weight at birth with accuracy comparable to human experts. Furthermore, combining estimates provided by human experts with those computed by BabyNet yields the best results, outperforming either of other methods by a significant margin. The source code of BabyNet is available at https://github.com/SanoScience/BabyNet.Comment: Early accepted for 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022, Singapor