Skin Disease Diagnosis Model Based on Multimodal Data with Explainable AI: The advancements of visual processing capabilities in modern computing have significantly accelerated the development of AI-driven systems aimed at enhancing the diagnosis and treatment of diseases. Since clinical decision-making often requires holistic consideration of various data types, the development of such systems necessitates robust methodologies for data integration and analysis of diverse medical and biological data collected from various sources. This project focuses on the development of an AI-based model for classification of skin diseases, leveraging multimodal data which includes medical images and patient-specific information. The model employs Convolutional Neural Networks (CNN) and Vision Transformer (ViT) architectures to achieve high accuracy in analyzing complex dermatological data. To enhance the interpretability and reliability of the model`s prediction, explainable AI techniques are incorporated to provide visual and feature-based explanations of the decision-making process. This system is designed to assist dermatology professionals and patients by offering an effective, accessible and user-friendly diagnostic tool.
Researcher: Berkay Üzer, Kerem Kunak / Advisor: Sema Candemir
Semi-Supervised and Explainable Deep Learning Model for the Classification of Gastrointestinal Diseases: This study explores the use of deep convolutional neural networks for diagnosing gastrointestinal diseases from endoscopic images using the HyperKvasir dataset. A noisy student self-training approach and focal loss are employed to tackle data imbalance, enhancing model performance. Explainable AI techniques, such as Grad-CAM, are used to highlight critical regions in the images, improving the interpretability of model decision.
Researcher: Mahmut Sami Yılmaz, Ahmet Emirhan Terzi, Seda Yeler / Advisor: Sema Candemir
Self-Training Segmentation for Medical Image Analysis: With the advancements in computer technology and its reflection in the field of health, deep learning-based techniques have revolutionized the diagnosis and treathment of many diseases. The success of deep learning-based techniques relies on large-scale annotated dataset. However, curating and annotating large amounts of medical data presents challenges related to patient privacy, data anonymization and domain expertise necessity for annotation. The purpose of this study is to enhance the segmentation performance on a dataset with limited annotation using a semi-supervised approach. Semi-supervised training combines labeled and unlabed data and enhances the model performance by inferring information from unlabeled dataset. The segmentation performance of the algorithm is being tested on CT sequences for successful Covid-19 lesion segmentation. This study is currently ongoing.
This research is financially supported by the Tubitak-BIDEB 2232 International fellowship program under the grant number 121C085.
Researchers: Göksu Turaç, Sema Candemir.
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Completed Research
2024 - Machine Learning-Based Model Development in Mobile Health Applications and Device-Cloud Deployment: Mobile Health allows health applications to be portable and enables mobile phone users to instantly access health solutions. In recent years, models developed with machine learning (ML) approaches have played an important role in healthcare in diagnosing and monitoring various diseases. However, the integration and execution of ML models, especially those based on image processing and deep learning, on mobile devices can be challenging due to the devices`s limited processing capacity and limited storage resources. This study describes the step-by-step development of a ML-based model, and its integration into mobile devices and cloud environments. Predicting skin disease has been chosen as a use-case mHealth model due to its usability. A MobileNet architecture hase been developed due to its suitability for mobile applications. Transfer learning technique, data augmentation and different loss functions have been considered to improve the model`s performance. The trained mHealth model is integrated into both device and cloud. All technical details of the process and a comparision of device-cloud intergration are provided.
Researcher: Özge Çiçek / Advisor: Sema Candemir
2024 - Explainable Artificial Intelligence-based Diagnosis of Lung Diseases from Chest X-rays: This project explores the utilization of Explainable Artificial Intelligence (XAI) techniques in the diagnosis of lung diseases from chest X-rays (CXR). The study uses the National Institutes of Health chest X-ray dataset containing lung pathologies: Atelectasis, Consolidation, Infiltration, Pneumothorax, Edema, Emphysema, Fibrosis, Effusion, Pneumonia, Pleural thickening, Cardiomegaly, Nodule, Mass, and Hernia. We have been developing a multi-class classifier based on convolutional neural networks integrated with an XAI component. The model has been learning the distinctive features in CXR associated with different lung diseases. The XAI module pinpoints the locations of these distinctive regions on CXR, thereby enhancing the interpretability of diagnostic decisions.
This research is supported by Tubitak-2209A Research Project Support Programme for Undergraduate Students
Researchers: İlayda Su İrday, Emirhan Sarp / Advisor: Sema Candemir
2023 - Deep Learning Models in Cognitive Diseases: Today`s technology empowers computers to process large amounts of medical data, enabling the development of AI-based systems. Especially, deep learning-based algorihtms have demonstrated performance comparable to expert decision-making, even uncovering previously unknown correlations in medical data. This talk will focus on using AI algorithms to identify, and assess the prognosis of cognitive impairment, Alzheimer`s disease and mild cognitive impairment. The talk will also address the challenges in the development of deep learning models for cognitive disease detection and prognosis.
Researchers: Sema Candemir, Demet Özbabalık Adapınar