7 papers
Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction
Zahra Asghari Varzaneh, Reza Khoshkangini, Magnus Johnsson +2
This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simultaneously in a single forward…
Context-Aware Hierarchical Bayesian Modeling of IVF Laboratory Environmental Conditions
Zahra Asghari Varzaneh, Reza Khoshkangini, Pia Saldeen +2
IVF pregnancy rates are routinely modeled using patient-level variables, while high-resolution laboratory environmental data remain underutilized. We show that this is a missed opp…
Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning
Zahra Asghari Varzaneh, Reza Khoshkangini, Thomas Ebner +1
Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology. While deep learning models offer automated analysis, most lack interpretability,…
Predicting Blastocyst Formation in IVF: Integrating DINOv2 and Attention-Based LSTM on Time-Lapse Embryo Images
Zahra Asghari Varzaneh, Niclas Wölner-Hanssen, Reza Khoshkangini +2
The selection of the optimal embryo for transfer is a critical yet challenging step in in vitro fertilization (IVF), primarily due to its reliance on the manual inspection of exten…
Multitasking Embedding for Embryo Blastocyst Grading Prediction (MEmEBG)
Nahid Khoshk Angabini, Mohsen Tajgardan, Mahesh Madhavan +3
Reliable evaluation of blastocyst quality is critical for the success of in vitro fertilization (IVF) treatments. Current embryo grading practices primarily rely on visual assessme…
An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
Mohsen Tajgardan, Atena Shiranzaei, Mahdi Rabbani +2
Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities rem…