Deep learning (DL) models have provided state-of-the-art performance in various medical
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …

Automatically segmenting tumors from brain magnetic resonance imaging scans is crucial
for diagnosis and planning treatment. However, brain tumors are highly diverse in location …

Abstract Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art
performance in medical image segmentation tasks. A common feature in most top …

Accurate segmentation of brain tumors in MRI sequences is an essential factor that helps
doctors make detailed surgery plans and evaluate prognoses. However, due to the diversity …

Brain tumors are one of the leading causes of death in adults. They come in various shapes
and sizes from one patient to another. Sometimes, they infiltrate surrounding normal tissues …

Accurate segmentation of different regions of gliomas from multimodal magnetic resonance
(MR) images is crucial for glioma grading and precise diagnosis, but many existing …

-Medical image segmentation plays an important role in disease monitoring, such as tumor
growth, dosage control of medication, and radiation exposure in the human body. Image …

Bien qu'il ait été démontré que les modèles d'apprentissage en profondeur (DL)
fonctionnent très bien sur diverses tâches d'imagerie médicale, l'inférence en présence de …