Feature extraction and representation has significant impact on the performance of any
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to …

There is a large body of literature linking anatomic and geometric characteristics of kidney
tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical
practice. However, most existing benchmarks and datasets only focus on segmentation …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …

Abstract Machine learning, particularly deep learning has boosted medical image analysis
over the past years. Training a good model based on deep learning requires large amount …

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the
potential to reduce medical costs, minimize stress to patients and make MRI possible in …

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research
field for many years. In the last decade, intensive developments in deep learning (DL) …

Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …