Abstract: Accurate liver and tumor segmentation in CT images is vital for diagnosis and treatment planning. This study presents SegResNet_2335, a lightweight 3D residual network optimized for ...

High-precision liver and tumor segmentation is a cornerstone of digital oncology, yet its clinical deployment remains constrained by two persistent challenges: the scarcity of pixel-level annotations ...

Data scarcity and class imbalance remain critical challenges in medical image analysis, particularly for brain tumor MRI segmentation, where subcomponents such as enhancing tumor, non-enhancing tumor, ...

medical_segmentation_system/ ├── api/ ├── classical_pipeline/ ├── data/ ├── dl_pipeline/ ├── evaluation/ ├── features ...

Exploring the Past and Current Landscape of Biomarker-Driven Clinical Trials Through Large Language Models First, we pretrained the encoder of a transformer-based network using a self-supervised ...

Medical image segmentation plays a vital role in diagnostic imaging, particularly for measuring brain tumor morphology in MRI scans, which directly influences treatment planning, prognosis, and ...

Abstract: The proposed work focuses on using LadderNet for Brain Tumor segmentation using MRI signals through the dataset as an input. The method is helpful in computerized medical analysis. Although ...