Important Notice for Prospective Lab Members

At BioMind AI Lab, we are committed to advancing AI-driven medical research through collaborations supported by robust data agreements. These agreements ensure compliance with ethical standards, privacy regulations, and institutional guidelines, granting access to high-quality datasets essential for our research. Below are our ongoing projects, a brief description of their goals, and their associated collaborations:

• MultiRadAI: A Multimodal AI Model for Prostate Cancer Diagnosis with Explainable AI: Developing a multimodal AI model to enhance diagnostic accuracy and interpretability in detecting prostate cancer.

Collaboration: AdventHealth

• Kidney Cancer Diagnosis and Prognosis Using CNNs and GANs: Leveraging AI to improve kidney cancer diagnosis, subtype classification, and disease outcome prediction.

Collaboration: AdventHealth

• Unsupervised Machine Learning for Vocal Fold Lesion Categorization: Using AI to objectively categorize benign vocal fold lesions and generate synthetic models for training.

Collaboration: Weill Cornell Medicine

• AI-Driven Pipeline for Cardiovascular Imaging in Congenital Heart Disease: Automating cardiovascular imaging analysis and report generation for better diagnosis and monitoring of CHD.

Collaboration: West Virginia University Medicine Children's Hospital

• Artificial Intelligence in Pancreatic Ductal Adenocarcinoma (PDAC): Multimodal Prediction of Survival, Metastatic Status, and Treatment Response: Building multimodal AI models that integrate clinical, genetic, histopathological, and radiological data to predict survival, metastatic spread, and therapy outcomes in pancreatic cancer. 

Collaboration: Charité – Universitätsmedizin Berlin

• Deep Neural Network for Automated Scoring of Estrogen Receptor Molecular Marker in Breast Cancer. Ebtisam Mohamed, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Fall 2022

• A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using MR Images. Mehnaz Hoque, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Spring 2023

• Brain Tumor Detection and Classification from MRI Images Using a Convolutional Neural Network Model. Raecine Greaves, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Spring 2023

• Classification of Normal versus Pneumonia from Chest X-ray Using an AI Model. Tassadit Lounes, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Spring 2023

• A Deep Learning Model for Detection of Alzheimer's Disease Based on MRI Images. WintPyae LynnHtaik, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Spring 2023

• Deep Learning-Based Detection of Breast Cancer Using Pathology Images. Anastasiya Sytina, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Fall 2023

• Automated Breast Tumor Segmentation in Ultrasound Images Using U-Net Algorithm. Elijah Zuniga, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Fall 2023

• Deep Learning Classification of Gastrointestinal Images. Nora Zougari, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Fall 2023

• Exploring a Breast Cancer Dataset: Analysis of Mass and Calcification Information. Victoria Jeter, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Fall 2023

• Classifying EEG Signals of Schizophrenia Using Neural Networks. Princess Jones, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech - Fall 2023

• Classifying Dermatoscopic Images of Skin Lesions Using Convolutional Neural Networks. Kevin Ramon Barrera, Biomedical Informatics (B.S.) Student, Department of Biological Sciences, City Tech – Summer 2025

Within every data point lies a heartbeat—a story waiting to be told. As we unravel the mysteries of the medical world, let’s remember that our work isn’t just about algorithms and codes; it’s about the lives we touch, and the futures we shape. Let’s venture into this journey not just as students of science but as architects of hope.