Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain

By Ruikai Zhang, Yali Zheng, T. Mak, Ruoxi Yu, S. Wong, J. Lau, Carmen C. Y. Poon. IEEE journal of biomedical and health informatics, 2017

333 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/26fae986973e2a83e798b2972e65c08b38f30bee

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. Xiaosong Wang
2. A. Cosa-Linan
3. Maryellen Lissak Giger
4. Berkman Sahiner
5. Hee E. Kim
6. Ronald M. Summers
7. Lubomir M. Hadjiiski
8. Karen Drukker

In Oncology

1. Ronald M. Summers

Citations in the corpus

• Deep learning in medical imaging and radiation therapy. (2018, methodology): “Different datasets also used for pretraining include CIFAR-10, 204 Places205, 304 and texture datasets, such as ALOT, DTD, FMD, and KTH-TIPS-2b, as discussed in the literature.”
• Transfer learning for medical image classification: a literature review (2022, methodology): “Similarly, fine-tuned CaffeNet by unfreezing each layer sequentially [45].”