BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix
SHEPHERD: Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
Code for "Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation". [AAAI 2021]
[ACL 2025 Findings] "Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA"
A Deep Learning Based approach for diagnosis of Schizophrenia using EEG brain recordings
This project leverages advanced AI agents from crewAI to assist doctors in diagnosing medical conditions and recommending treatment plans based on patient-reported symptoms and medical history. The solution uses Streamlit for the user interface and crewai library to define and manage AI agents and tasks.
A Vietnamese dataset of over 12 thousands questions about common disease symptoms. Perfect for researchers and developers building Vietnamese healthcare chatbots or disease prediction models.
Early detection of Autism Spectrum Disorder (ASD) is crucial for children's development, yet the diagnostic procedure remains challenging. EyeTism employs machine learning on eye tracking data from both high-functioning ASD and typically developing children (TD) to create a diagnostic tool based on their distinct visual attention patterns.
Using TensorFlow Object Detection API to detect blood cells
Clustering Analysis of all available research data on the Iowa Gambling Task(list of sources in readme) using R. The Scripts produce the output for the most common archetypes among the dataset of one researcher using PCA.
Code for my Master Thesis project on "Prompting Techniques for Natural Language Generation in the Medical Domain" at the University of Bologna
Early detection of Autism Spectrum Disorder (ASD) is crucial for children's development, yet the diagnostic procedure remains challenging. EyeTism employs machine learning on eye tracking data from both high-functioning ASD and typically developing children (TD) to create a diagnostic tool based on their distinct visual attention patterns.
A RESTful API using Flask and XGBoost to predict diabetes in Pima Indians based on various diagnostic measurements. Includes training, saving the best model, and testing the API using Python requests.
❤️ Likelihood of heart disease it analyzes user inputs such as age, gender, cholesterol levels, and other clinical features to provide accurate predictions.
Supervised Contrastive Learning for Diabetic Retinopathy Classification
In this repository, you'll find Prolog code covering a range of questions, all designed to aid your learning journey.
The personalised healthcare system uses machine learning to provide tailored treatment recommendations based on patient data. It analyzes medical history and lifestyle factors to predict health risks and suggest preventive care. The system enhances patient outcomes through data-driven insights.
Dyslipidemia, a condition with abnormal lipid levels in the blood, significantly increases the risk of cardiovascular diseases like heart attacks and strokes. This project aims to build accurate models for predicting dyslipidemia using both machine learning (ML) and deep learning (DL) techniques. The primary focus is on maximizing recall to minimiz
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