MY WORK
AMARA (PhD Project)
X-Ray Quality Control
Breast Cancer Diagnosis
Kilometer Tracker
Balance App
- Project Description
Deep learning has revolutionized artificial intelligence, with Radboudumc creating an algorithm for pulmonary nodule malignancy prediction that matches thoracic radiologist performance on low-dose chest CT.
To apply this algorithm in routine clinical practice, Radboudumc aims to enhance the current algorithm with additional CT data and will validate it against data from European lung cancer screening trials and five Dutch institutes.
The ultimate goal is that this AI tool will speed up the diagnosis of malignant nodules and reduce unnecessary nodule examinations.
Next to the development of this new algorithm, we aimed to further enhance its safety and trustworthiness with uncertainty estimation and out-of-distribution detection. Find a full description of my PhD project at DIAG Nijmegen. - The LUNA25 Challenge
The LUNA25 challenge is a benchmarking initiative aimed at evaluating the performance of artificial intelligence (AI) algorithms and radiologists in lung cancer screening using computed tomography (CT) scans. The challenge provides a standardized dataset and evaluation framework to assess the accuracy, sensitivity, and specificity of AI models in detecting lung nodules and predicting malignancy risk. By comparing AI algorithms with human radiologists, the LUNA25 challenge aims to advance the development of reliable and effective AI tools for lung cancer screening, ultimately improving early detection and patient outcomes.
Participants in the challenge had the opportunity to showcase their AI models and contribute to the growing body of knowledge in the field of medical imaging and lung cancer diagnosis. More information about the challenge can be found at LUNA25 Challenge.
- Deep Learning Uncertainty Estimation
An important aspect for safe clinical implementation of a Deep Learning algorithm is the ability to gauge and communicate uncertainty. Uncertainty Estimation can help to identify situations where the algorithm has doubts about its predictions.
Leveraging this uncertainty estimation can optimize the clinical workflow by preventing the algorithm from making a lung cancer diagnosis in cases with high uncertainty and refer these the clinical experts for further evaluation.
This proactive integration of uncertainty estimation holds promise to minimize algorithm mistakes, thereby elevating the safety profile of a clinically adopted algorithm. - Out-of-Distribution Detection
Deep learning models may fail and suffer from reduced diagnostic performance when applied to unseen and abnormal data that varies from the training distribution. The reduced performance is even more evident on out-of-distribution (OOD) data that can be a result of changes in patient demographic, disease incidence or image acquisition and reconstruction. These changes are a phenomenon known as dataset shift.
Incorporating an OOD detection component into the Deep learning model can help in identifying dataset shifts and ensure safe and reliable clinical implementation.
- Project Description
The LRCB is the National expert center that performs the quality control of medical devices used in the Dutch Screening program for breast cancer and tuberculosis. The current quality control procedure of phantom images obtained with mammographic devices used in the Dutch breast cancer screening program is time consuming and observer dependent. During the quality control, physicists manually analyze phantom images for visible artefacts. With the use of deep learning algorithms, we aimed to classify phantom images with visible artefacts. - Experiments
In this project, we used several pretrained models to classify phantom images for a binary task (visible vs non-visible artefact) and a multi-class task (8 artefact classes vs non-visible). - Outcome
The trained models showed to be accuracte in the classification of artefacts for both the binary and multi-class classification task. These promising results demonstrated the potential deep learning has in the quality control of X-ray devices.
- Project Description
In the diagnosis of microcalcifications, linked to breast cancer, most patients undergo a biopsy since radiologists find it challenging to reliably discern between benign and malignant lesions. Most of these biopsies reveal benign results, causing unnecessary stress for the patients and increased diagnostic costs. With the use of machine learning algorithms, we aimed to develop a microcalcification malignancy risk estimation predictor. - Experiments
In this project, we extracted radiomics features from microcalcifications on low-energy contrast enhanced spectral mammography images to develop multiple machine learning models. The models were trained with and without including clinical parameters. - Outcome
The models showed an excellent distriminative performance in the classification of benign and malignant microcalcifications, which demonstrated its potential to improve clinical care and reduce unnecessary biopsies.
-
Project Description
Sharing a car with someone close, like a partner or colleague, made me realize that it can be important to track the kilometers driven by each person and fairly distribute fuel expenses. This inspired me to develop a practical and user-friendly kilometer tracker. The application enables users to register their trips, monitor kilometers driven, and keep track of fuel payments, ensuring transparency and fairness. Built as a real-world solution, this project combines technologies including Python, Django, HTML, and CSS, with hosting capabilities for seamless online access. -
Outcome
The result is a fully functional online application designed to simplify trip and expense tracking for car-sharing users. Its features include:- User Authentication
- Create User Groups
- Register Trips and Payment per Group
- Overview of all Trips and Payments
- Option to Balance the Group
- Project Description
Keeping track of recipes, meal planning, and grocery shopping can be time-consuming and overwhelming. To address this, I am developing a mobile recipe management and meal planning application using tools like JavaScript, React, and Python. - Outcome (in progress)
A recipe management and meal planning application with an AI-powered chat bot that helps users to track their recipes, plan meals, and generate shopping lists based on selected recipes.
