MS-Net: Mixed-Supervision Fully-Convolutional Networks for Full-Resolution Segmentation

By Meet Shah, S. Merchant, Suyash P. Awate. 2018

57 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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. Zhiding Yu
2. Lequan Yu
3. Holger R. Roth
4. Alan L. Yuille
5. Luc Van Gool
6. Daguang Xu
7. Dong Yang
8. Zhuotun Zhu

Citations in the corpus

• Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation (2019, background): “Some studies show that connections between global features are pivotal [11].”
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020, background): “Moreover, mixed supervisions [42,54] combining dense label masks and weak labels like bounding boxes, slice- or image-level labels, etc.”
• Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection (2018, unknown): “In contrast, we propose a FPN-based one-stage detector, which allows to naturally fuse object detection and segmentation, resulting in the simple Retina U-Net architecture.”