most citedIntroducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection

Arezoo Borji, Gernot Kronreif, Bernhard Angermayr +6

Breast cancer is a highly heterogeneous disease with diverse molecular profiles. The PAM50 gene signature is widely recognized as a standard for classifying breast cancer into intr…

cs.AI2024

An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models

Arezoo Borji, Hossam Haick, Birgit Pohn +7

In vitro fertilization (IVF) is a widely utilized assisted reproductive technology, yet predicting its success remains challenging due to the multifaceted interplay of clinical, de…

eess.IV2024

A Review on the Integration of Artificial Intelligence and Medical Imaging in IVF Ovarian Stimulation

Jana Zakall, Birgit Pohn, Antonia Graf +6

Artificial intelligence (AI) has emerged as a powerful tool to enhance decision-making and optimize treatment protocols in in vitro fertilization (IVF). In particular, AI shows sig…

eess.IV2024

Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images

Arezoo Borji, Gernot Kronreif, Bernhard Angermayr +1

Recent advances in machine learning are transforming medical image analysis, particularly in cancer detection and classification. Techniques such as deep learning, especially convo…

eess.SP2024★ 1 cited

Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images

Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

Alzheimer's disease is a progressive neurodegenerative disorder that primarily affects cognitive functions such as memory, thinking, and behavior. In this disease, there is a criti…

cs.LG2024

A learning-based solution approach to the application placement problem in mobile edge computing under uncertainty

Taha-Hossein Hejazi, Zahra Ghadimkhani, Arezoo Borji

Placing applications in mobile edge computing servers presents a complex challenge involving many servers, users, and their requests. Existing algorithms take a long time to solve…