Index

Surrogate Model for Physics Informed Lifetime Prediction in Power Electronics based on Mission Profiles

Evaluating the Effectiveness of Large Language Models in Named Entity Recognition in Specialized Industrial Domain

Vision-Language Models for Pathology Report Generation from Gigapixel Whole-Slide Images

Dynamic Gap Closure Forecasting in the DAX Index

Evaluation and optimization of an implicit neural representation framework for markerless tumor tracking during radiotherapy

In the radiotherapy of tumors, a precise definition of the tumor volume is essential, in order to keep the radiation exposure of the surrounding tissue as low as possible. For this purpose, a planning CT scan is taken before treatment, which is used to define the area to be irradiated, also known as the planning target volume (PTV). The PTV is always chosen to be larger than the actual volume of the tumor in order to ensure that a sufficiently high dose is applied despite uncertainties such as positioning or movement of the tumor volume due to respiration. [1] Especially in the thorax and abdomen, the intrafractional movement due to respiration and physiological changes is very high. In order to compensate for this, the respiratory movement can be measured using external surrogates and imaging techniques and its extent can also be restricted using special breathing techniques. However, these methods only allow an indirect conclusion to be drawn about the tumor position. Although it is possible to measure the movement of the tumor using implanted markers, this is an additional invasive procedure, which is associated with corresponding risks and delays the start of treatment. [2] For the most accurate description of tumor movement, it would be advantageous to automatically segment the tumor on the fluoroscopic x-ray images of the linear accelerator and track its position in real-time. Since the low soft tissue contrast in the fluoroscopy projection images impedes distinguishing the tumor from surrounding structures, tracking the tumor in a synthesized 3D scan volume and then projecting its location onto the 2D x-ray image could improve the segmentation quality. Shao et al. [3] have recently presented this kind of approach with the dynamic reconstruction and motion estimation (DREME) framework. During training they divide the 3D tracking into 2 separate tasks, first a motion estimation consisting of a CNN encoder and a B-spline- based interpolant and second the reconstruction of a reference CBCT scan from the pre-treatment dynamic CBCT projections using implicit neural representations (INR). During inference, the network gets the x-ray projections as input to estimate the motion and deform the reference CBCT volume to synthesize the current real-time CBCT. [3] The goal of this thesis is to re-implement the DREME framework and evaluate its performance on our own dataset of abdominal tumors, since in their paper, Shao et al. [3] report results only on a digital phantom and a lung dataset. Furthermore their reported training time of 4 hours is not feasible in the current clinical workflow, therefore this thesis aims to explore optimization techniques, e.g. pre-training on the planning CT, to reduce the training time.

The thesis will include the following points:

Literature review on INR deep learning methods;
Implementation of the DREME framework for real-time motion tracking;
Performance evaluation on our fluoroscopy dataset;
Exploration of different strategies to reduce the training time (e.g. pre-training on the planning CT or other patients, higher parallelization, selective loss computation, etc.).

If you are interested in the project, please send your request to: pluvio.stephan@uk-erlangen.de

Prior experience in Python, Deep Learning and PyTorch is required.

Exploring RNN-Transducers for Named Entity Recognition in Biomedical Literature

Shallow Networks and AI Explainability in Context of vDCE for Breast MRI

Introduction
Dynamic Contrast-Enhanced MRI (DCE-MRI) is a key tool in breast cancer diagnostics, offering detailed vascular information essential for identifying and evaluating tumors [1]. However, ontrast agents used in this process can pose risks, particularly for patients with kidney issues or allergies [2]. Virtual Dynamic Contrast Enhancement (vDCE) provides a promising alternative by enerating contrast-enhanced images computationally, removing the need for actual contrast agents [3]. This thesis explores improving vDCE through smaller, more interpretable neural and dynamic network architectures, focusing on better resource explainability.

Motivation
Smaller, shallow neural networks offer several advantages, such as:
• Lower Computational Needs: Shallow models require less processing power, making them ideal for limited-resource environments [4].
• Localized Analysis: These models can focus on specific regions, such as individual breast areas, which improves diagnostic accuracy [5].
• Enhanced Transparency: Simpler architectures provide greater clarity in their decision-making process, making results easier for clinicians to interpret [6].
• Since insights derived from one breast often do not affect the other, this localized and interpretable approach is particularly well-suited for breast MRI analysis.

Objectives
• Develop and Test Shallow Neural Network Models for vDCE: Design models that balance accuracy with simplicity [4].
• Implement Explainability Tools: LIME, and SHAP to make model decisions clearer to clinicians [6].
• Explore the Efficiency-Accuracy Trade-off: Examine how smaller models can maintain diagnostic accuracy while being computationally efficient.
• Explore patch-based approaches:
Methodology
• New Network Architectures: Investigate linear models, dynamic convolutions, hypernetworks, and attention mechanisms to optimize shallow networks.
• Explainability Methods: Apply, LIME, and SHAP for clearer decision insights. This includes exploring the impact of patch size on capturing spatial context and analyzing the significance of specific input features on model’s decision making.
• Performance Metrics: Compare shallow models against deeper models for accuracy, efficiency, and clarity. The evaluation will include a metrics-based comparison with state-of-the-art methods [3] and a reader study involving radiologists to assess the clinical relevance and usability of the outputs

References
1. Turnbull, L.W. (2009), Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer. NMR Biomed., 22: 28-39. https://doi.org/10.1002/nbm.1273
2. Andreucci M, Solomon R, Tasanarong A. Side effects of radiographic contrast media: pathogenesis, risk factors, and prevention. Biomed Res Int. 2014;2014:741018. doi: 10.1155/2014/741018. Epub 2014 May 11. PMID: 24895606; PMCID: PMC4034507.
3. Schreiter, Hannes, et al. “Virtual dynamic contrast enhanced breast MRI using 2D U-Net Architectures.” medRxiv (2024): 2024-08.
4. Prinzi, F., Currieri, T., Gaglio, S. et al. Shallow and deep learning classifiers in medical image analysis. Eur Radiol Exp 8, 26 (2024). https://doi.org/10.1186/s41747-024-00428-2
5. van der Velden, B.H.M., Janse, M.H.A., Ragusi, M.A.A. et al. Volumetric breast density estimation on MRI using explainable deep learning regression. Sci Rep 10, 18095 (2020). https://doi.org/10.1038/s41598-020-75167-6
6. Gulum, M.A.; Trombley, C.M.; Kantardzic, M. A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging. Appl. Sci. 2021, 11, 4573. https://doi.org/10.3390/app11104573