Detection of Red lesions in diabetic retinopathy affected fundus images
By Vijay M. Mane, Ramish B. Kawadiwale, Dattatray V. Jadhav. 2015
37 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
- Gwenolé Quellec
- Sven Behnke
- Mathieu Lamard
- Béatrice Cochener
- Yassine Boudi
- Jörg Wagner
- Leon Hetzel
- Thaddäus Wiedemer
In Ophthalmology
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, methodology): “The methods [54, 28, 21, 29] use supervised approaches on image level without reporting a localization.”
- Deep image mining for diabetic retinopathy screening (2016, background): “Note that all competing solutions (Kauppi et al., 2007; Yang et al., 2013; Franklin and Rajan, 2014; Kumar et al., 2014; Bharali et al., 2015; Mane et al., 2015; Dai et al., 2016) are trained at the lesion level, while ours is trained (in Kaggle-train) at the image level.”
- Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images (2017, unknown): “[35] - - - 96.”