Computer-aided tumor detection in endoscopic video using color wavelet features

By Stavros A. Karkanis, Dimitrios K. Iakovidis, D. Maroulis, Dimitrios Alexios Karras, M. Tzivras. IEEE Transactions on Information Technology in Biomedicine, 2003

520 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/7006fd2baa6ceca7209706fc28a7034e3eacaa78

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Huan Fu
2. Tao Zhou
3. Jianming Liang
4. Jae Y. Shin
5. Chen Gong
6. R. Todd Hurst
7. Michael B. Gotway
8. Nima Tajbakhsh

In Oncology

1. Huan Fu
2. Nima Tajbakhsh
3. Suryakanth R. Gurudu
4. Jianming Liang
5. Radu Marculescu
6. Md Mostafijur Rahman

Citations in the corpus

• Cross-level Feature Aggregation Network for Polyp Segmentation (2023, methodology): “Further, the co-variances of texture measurements are used to represent different polyp regions [24] .”
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? (2016, background): “The early systems [49]–[51] relied on polyp color and texture for detection.”
• Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information (2016, methodology): “[12] was published in 2003 where color wavelet features coupled with a slidingwindowwas used for detecting polyps in colonoscopy images.”