Medical Image Segmentation Using Deep Neural Networks with Pre-trained Encoders

By Alexandr A Kalinin, Vladimir I. Iglovikov, Alexander Rakhlin, Alexey A. Shvets. 2020

47 citations recorded by the source corpus.

Subfields: Gastroenterology, Oncology

How researchers see this paper

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

In Gastroenterology

1. A. Ben Hamza
2. Hasib Zunair

In Oncology

1. A. Ben Hamza
2. Hasib Zunair

Citations in the corpus

• Medical Image Segmentation Using Deep Learning: A Survey (2020, unknown): “Kalinin [109] et al.”
• Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation (2021, methodology): “We also scale Sharp U-Net by replacing the encoder with a pretrained convolutional neural network (e.g., VGG [15] or ResNet [23]) on the 1000-class Imagenet dataset, as the encoder plays the role of a feature extractor.”