Speaker
Description
Artificial Intelligence (AI) has seen a significant surge in popularity, particularly in its application to medicine. The exponential increase in examinations by 3D imaging devices such as CT and MRI has resulted in a massive volume of image data, necessitating the use of AI. Typically, doctors interpret these scans in a time-consuming process often limited by subjectivity, image complexity, inter-observer variability, and fatigue.
In collaboration with AGEL Radiologia s.r.o. in St. Cyril and Methodius Hospital in Bratislava, we are developing methods for diagnosing various diseases from CT and MR images, focusing on detecting leukoencephalopathy and meningiomas, as well as prostate cancer and kidney stones. Using a dataset of approximately 1,200 patients with axial brain CT scans, we trained convolutional neural networks (CNNs) for binary leukoencephalopathy classification and achieved a classification accuracy of 98.5%. We also achieved 88% accuracy for prostate segmentation and 68% accuracy for prostate cancer classification.
We emphasize the importance of simplifying dataset creation by healthcare professionals, ensuring that doctors only perform tasks they would typically do during standard diagnoses. We do not require any additional work from doctors to create the labeling for the dataset; instead, we record the data during their routine work. This approach introduces technical challenges in preprocessing, such as converting measured locations into bounding boxes and segmentation based on density differences, which require meticulous technical handling.
Our research also addresses the critical issue of transparency and interpretability in AI models. To gain insights into model decision-making, we implemented Grad-CAM heatmaps, which highlight the focus areas of the models on the scans. We plan to incorporate other explainable neural network components, such as feature importance and attention mechanisms, to help medical professionals understand why AI systems make specific decisions. This approach increases trust and facilitates the adoption of AI in clinical practice.
Future research plans include the use of semi-supervised learning methods, which are particularly important when working with medical images due to the scarcity of annotated data. Our plans also include continuous self-learning techniques for AI models, allowing them to adapt and improve as new data becomes available. This approach ensures that the AI system remains up-to-date and maintains high accuracy, which is crucial in healthcare.
