Uniqueness-Driven Saliency Analysis for Automated Lesion Detection with Applications to Retinal Diseases

By Yitian Zhao, Yalin Zheng, Yifan Zhao, Yonghuai Liu, Zhili Chen, Peng Liu, Jiang Liu. 2018

28 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

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. Lawrence Carin
2. Ricardo Henao
3. Fanjie Kong
4. Sven Behnke
5. Yuewei Yang
6. Jörg Wagner
7. Leon Hetzel
8. Dong Wang

In Ophthalmology

1. Jan M. Köhler

In Radiology, Nuclear Medicine and Imaging

1. Lawrence Carin
2. Jan M. Köhler

Citations in the corpus

• Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, background): “[51] propose an unsupervised approach to extract salient regions.”
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models (2020, background): “Among these, two streams of work, namely saliency mapping (SM) [65, 52, 10] and causal representation learning (CRL) [29, 62, 2], stand out as some of the most promising directions.”