Mohammad Yaqub – författare

This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: ​image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.

This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. 

The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: ​image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.

This book constitutes the refereed proceedings of the 25th Conference on Medical Image Understanding and Analysis, MIUA 2021, held in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 32 full papers and 8 short papers presented were carefully reviewed and selected from 77 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging.

This book constitutes the refereed proceedings of the 25th Conference on Medical Image Understanding and Analysis, MIUA 2021, held in July 2021. Due to COVID-19 pandemic the conference was held virtually. 

The 32 full papers and 8 short papers presented were carefully reviewed and selected from 77 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging.

This book constitutes the proceedings of the MICCAI 2025 Grand Challenge on Intrapartum Ultrasound, IUGC 2025, which was held in conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.The 9 full papers 1 short paper and  presented in this volume were carefully reviewed and selected from 20 submissions. They are grouped into the following topics:  Top-performing solutions explored semi-supervised frameworks; Self-supervised pretraining; Transformer-based backbones; Adversarial learning; Pseudo-labeling strategies.

This book constitutes the proceedings of the MICCAI 2025 Grand Challenge on Intrapartum Ultrasound, IUGC 2025, which was held in conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.The 9 full papers 1 short paper and  presented in this volume were carefully reviewed and selected from 20 submissions. They are grouped into the following topics:  Top-performing solutions explored semi-supervised frameworks; Self-supervised pretraining; Transformer-based backbones; Adversarial learning; Pseudo-labeling strategies.

This book constitutes the 4th 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2025, which was held in conjunction with the 28th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2026, on September 2025.The 15 full papers included in this book were carefully reviewed and selected from 15 submissions. They were organized in topical sections as follows: Tumor and Lymph Node Segmentation;  Joint Learning: Segmentation with Prognosis and/or HPVPrediction; and Downstream Prediction from PET/CT and Clinical Data.