Weakly-Supervised Evidence Pinpointing and Description

By Qiang Zhang, Abhir H. Bhalerao, Charles E. Hutchinson. 2017

20 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Oral Surgery

How researchers see this paper

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

In Biomedical Engineering

1. Ender Konukoglu

In Computer Vision and Pattern Recognition

1. Ender Konukoglu
2. Christian F. Baumgartner
3. Jia Xi Ang
4. Lisa M. Koch
5. Kerem Can Tezcan

Citations in the corpus

• Visual Feature Attribution Using Wasserstein GANs (2017, background): “Pinpointing all evidence of a class is important for a variety of tasks such as weakly supervised localisation or segmentation of structures [41, 43, 65], and better understanding disease effects, and physiological or pathological processes in medical images [67, 18, 12, 19, 13, 27, 54, 30, 31, 63].”
• DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning (2018, methodology): “Previous work on computer-aided diagnosis of spinal stenosis used only axially-or only sagittally-oriented MRI slices as imaging inputs (Zhang et al., 2017; Jamaludin et al., 2017a,b).”