Index

Fetal Re-Identification: Deep Learning on Pregnancy Ultrasound Images

Project description

Accurate analysis of ultrasound images during pregnancy is important for monitoring fetal development and detecting abnormalities. For better accuracy and time convenience, the help of artificial intelligence or accordingly deep learning is useful [1]. However, currently there is less research in deep learning in the field of ultrasound imaging compared to MRI or CT images [2].

Considering its non-invasive nature, lower cost, and lower risk to patients compared to other modalities such as MRI or CT, ultrasound imaging is the most commonly used method to assess fetal development and maternal health [3]. Overall, the correct acquisition of fetal ultrasound data is difficult and time-consuming. Deep Learning can help to reduce examiner dependence, and improve analysis as well as maternal-fetal medicine in general [1].

Although literature on fetal ultrasound imaging in conjunction with deep learning exists [4-6], little previous work investigated fetal re-identification. In multiple pregnancies with fetuses of the same sex or early in pregnancy, the fetuses cannot be distinguished. Therefore, the fetuses are assigned an order at physician descretion (usually based on position in the mother’s womb), although it is not clear whether this order preserves during subsequent examinations. However, this information is important because the risk of fetal abnormalities is greater in multiple pregnancies than in singleton pregnancies [7]. In addition, a good representation of the fetus is also eminent for the emotional connect of the parents [8].

Consequently, the aim of this thesis is an early feasibility investigation of re-identification approaches in fetal ultrasound.

 

References

[1] J. Weichert, A. Rody, and M. Gembicki. Zukünftige Bildanalyse mit Hilfe automatisierter Algorithmen. Springer Medizin Verlag GmbH, 2020.

[2] Xavier P. Burgos-Artizzu, David Coronado-Guiérrez, Brenda Valenzuela-Alcaraz, Elisenda Bonet-Carne, Elisenda Eixarch, Fatima Crispi, and Eduard Gratacos. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Nature Scientific Reports, 2020.

[3] D. Selvathi and R. Chandralekha. Fetal biometric based abnormality detection during prenatal development using deep learning techniques. Springer, 2021.

[4] Jan Weichert, Amrei Welp, Jann Lennard Scharf, Christoph Dracopoulos, Achim Rody, and Michael Gembicki. Künstliche Intelligenz in der pränatalen kardialen Diagnostik. page 10, 2021.

[5] Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P. Fletcher, Sandra Smith, Lisa M. Koch, Bernhard Kainz, and Daniel Rueckert. SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound. page 12, 2017.

[6] Juan C. Prieto, Hina Shah, Alan J. Rosenbaum, Xiaoning Jiang, Patrick Musonda, Joan T. Price, Elizabeth M. Stringer, Bellington Vwalika, David M. Stamilio, and Jeffrey S. A. Stringer. An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation. page 11, 2021.

[7] R Townsend and A Khalil. Ultrasound surveillance in twin pregnancy: An update for practitioners. Ultrasound, 2018.

[8] Tejal Singh, Srinivas Rao Kudavelly, and Venkata Suryanarayana. Deep Learning Based Fetal Face Detection And Visualization In Prenatal Ultrasound. 2021.

Machine Learning Based Analysis of EEG Data Recorded during Stimulation with Continuous Speech

Motif analysis of resting-state and stimulus driven fMRI networks with special focus on functional role of the motifs

Adaptive Training of Heat Demand Prediction using Continual Learning

One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. On the track of facilitating the energy transition and mitigating the anthropomorphic climate change, framework for heat demand forecast poses the basis for different applications, impacting the operational as well as the economical efficiency of a utility company. More concretely, VK Energy [1] optimize combined heat and power (CHP) generation systems to increase their overall efficiency and flexibility. In order to operate the CHP system in heating networks with heat storage adaptively to the demand of the electricity system, a real-time, accurate, robust and user friendly (i.e., ideally without extensive hyperparameter tuning) forecast of the heat demand in the respective heating network is indispensable.

As heat demand forecast is an on-going research topic, numerous methods [2-5] have been proposed in the recent years. Although advanced machine learning (ML) and deep learning (DL) methods proposed in the state-of-the-arts demonstrate tremendous capability to predict the heat demand accurately, their performance is limited by the training data. As the heat consumption strongly depends on the weather condition, which is a dynamic environment, ML/DL models trained initially may not be valid anymore with changing consumption behavior and climate. Therefore, continual learning paradigm [6] should be considered to improve the applicability of ML/DL algorithms for heat demand forecast.

In this thesis, following aspects need to be considered:

  • Literature review of heat consumption prediction and continual learning.
  • Development and implementation of a continual learning framework for heat demand prediction comprising different forecasting methos and retraining mechanism.
  • Comprehensive evaluation of the performance of the implemented framework w.r.t. accuracy, robustness, run-time, and potentially usability.

[1] https://www.vk-energie.de/

[2] Y. Zhao, Y. Shen, Y. Zhu and J. Yao, “Forecasting Wavelet Transformed Time Series with Attentive Neural Networks,” 2018 IEEE International Conference on Data Mining (ICDM), 2018, pp. 1452-1457, doi: 10.1109/ICDM.2018.00201.

[3] Chatterjee, Satyaki and Bayer, Siming and Maier, Andreas K “Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model”, NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021, https://www.climatechange.ai/papers/neurips2021/42

[4] Kováč, Szabolcs and Micha’čonok, German and Halenár, Igor and Važan, Pavel,” Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network”, Special Issue “Artificial Intelligence in the Energy Industry”, https://www.mdpi.com/1996-1073/14/6/1545

[5] Chatterjee S., Ramachandran A., Neergaard TF., Maier A., Bayer S., Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble. In: NeurIPS 2022 Workshop Tackling Climate Change with Machine Learning, 2022

[6] https://towardsdatascience.com/how-to-apply-continual-learning-to-your-machine-learning-models-4754adcd7f7f

Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography

Optical Character Recognition on Technical Drawings using Deep Learning

Offline-to-Online Handwriting Translation using Cyclic Consistency

Emotion Recognition in Comic Scenes with Multimodal Classifiers

Investigation of biases in acoustic embeddings for the detection of Alzheimer’s disease

Alzheimer’s disease is one of the most common neurodegenerative disorders that greatly impact individual and societal levels. These patients not only suffer from dementia but also from depression which can lead to more decline in cognitive abilities. However, both AD and depression have some common symptoms that make the detection of depression in Alzheimer’s extremely challenging. But several studies have used subsets of the DementiaBank database and employed different audio embeddings to detect depressive AD patients. Nevertheless, such embeddings can be biased for non-clinical factors.

Multi-View CBCT Projections Generation Using Conditional Score-Based Generative Model