43 research outputs found
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
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
Tissue characterization has long been an important component of Computer
Aided Diagnosis (CAD) systems for automatic lesion detection and further
clinical planning. Motivated by the superior performance of deep learning
methods on various computer vision problems, there has been increasing work
applying deep learning to medical image analysis. However, the development of a
robust and reliable deep learning model for computer-aided diagnosis is still
highly challenging due to the combination of the high heterogeneity in the
medical images and the relative lack of training samples. Specifically,
annotation and labeling of the medical images is much more expensive and
time-consuming than other applications and often involves manual labor from
multiple domain experts. In this work, we propose a multi-stage, self-paced
learning framework utilizing a convolutional neural network (CNN) to classify
Computed Tomography (CT) image patches. The key contribution of this approach
is that we augment the size of training samples by refining the unlabeled
instances with a self-paced learning CNN. By implementing the framework on high
performance computing servers including the NVIDIA DGX1 machine, we obtained
the experimental result, showing that the self-pace boosted network
consistently outperformed the original network even with very scarce manual
labels. The performance gain indicates that applications with limited training
samples such as medical image analysis can benefit from using the proposed
framework.Comment: accepted by 8th International Workshop on Machine Learning in Medical
Imaging (MLMI 2017
Simulation of the Beating Heart Based on Physically Modeling aDeformable Balloon
The motion of the beating heart is complex and createsartifacts in SPECT and x-ray CT images. Phantoms such as the JaszczakDynamic Cardiac Phantom are used to simulate cardiac motion forevaluationof acquisition and data processing protocols used for cardiacimaging. Two concentric elastic membranes filled with water are connectedto tubing and pump apparatus for creating fluid flow in and out of theinner volume to simulate motion of the heart. In the present report, themovement of two concentric balloons is solved numerically in order tocreate a computer simulation of the motion of the moving membranes in theJaszczak Dynamic Cardiac Phantom. A system of differential equations,based on the physical properties, determine the motion. Two methods aretested for solving the system of differential equations. The results ofboth methods are similar providing a final shape that does not convergeto a trivial circular profile. Finally,a tomographic imaging simulationis performed by acquiring static projections of the moving shape andreconstructing the result to observe motion artifacts. Two cases aretaken into account: in one case each projection angle is sampled for ashort time interval and the other case is sampled for a longer timeinterval. The longer sampling acquisition shows a clear improvement indecreasing the tomographic streaking artifacts
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
In this paper, we propose an end-to-end multi-task neural network called
FetalNet with an attention mechanism and stacked module for spatio-temporal
fetal ultrasound scan video analysis. Fetal biometric measurement is a standard
examination during pregnancy used for the fetus growth monitoring and
estimation of gestational age and fetal weight. The main goal in fetal
ultrasound scan video analysis is to find proper standard planes to measure the
fetal head, abdomen and femur. Due to natural high speckle noise and shadows in
ultrasound data, medical expertise and sonographic experience are required to
find the appropriate acquisition plane and perform accurate measurements of the
fetus. In addition, existing computer-aided methods for fetal US biometric
measurement address only one single image frame without considering temporal
features. To address these shortcomings, we propose an end-to-end multi-task
neural network for spatio-temporal ultrasound scan video analysis to
simultaneously localize, classify and measure the fetal body parts. We propose
a new encoder-decoder segmentation architecture that incorporates a
classification branch. Additionally, we employ an attention mechanism with a
stacked module to learn salient maps to suppress irrelevant US regions and
efficient scan plane localization. We trained on the fetal ultrasound video
comes from routine examinations of 700 different patients. Our method called
FetalNet outperforms existing state-of-the-art methods in both classification
and segmentation in fetal ultrasound video recordings.Comment: Accepted to 28th International Conference on Neural Information
Processing (ICONIP) 2021, Bali, Indonesia, 8-12 December, 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