File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/EMBC.2018.8512828
- Scopus: eid_2-s2.0-85056640060
- PMID: 30440966
- Find via
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading
Title | Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading |
---|---|
Authors | |
Keywords | Deep Learning Diabetic Retinopathy Medical Image Multi-Cell Architecture MultiTask Learning |
Issue Date | 2018 |
Citation | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, v. 2018-July, p. 2724-2727 How to Cite? |
Abstract | Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a Multi-Cell architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy. Further, considering the different stages of DR actually progress gradually, which means the labels of different stages are related. To considering the relationships of images with different stages, we propose a Multi-Task learning strategy which predicts the label with both classification and regression. Experimental results on the Kaggle dataset show that our method achieves a Kappa of 0.841 on test set which is the 4th rank of all state-of-the-arts methods. Further, our Multi-Cell Multi-Task Convolutional Neural Networks (M2CNN) solution is a general framework, which can be readily integrated with many other deep neural network architectures. |
Persistent Identifier | http://hdl.handle.net/10722/345238 |
ISSN | 2020 SCImago Journal Rankings: 0.282 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhou, Kang | - |
dc.contributor.author | Gu, Zaiwang | - |
dc.contributor.author | Liu, Wen | - |
dc.contributor.author | Luo, Weixin | - |
dc.contributor.author | Cheng, Jun | - |
dc.contributor.author | Gao, Shenghua | - |
dc.contributor.author | Liu, Jiang | - |
dc.date.accessioned | 2024-08-15T09:26:05Z | - |
dc.date.available | 2024-08-15T09:26:05Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, v. 2018-July, p. 2724-2727 | - |
dc.identifier.issn | 1557-170X | - |
dc.identifier.uri | http://hdl.handle.net/10722/345238 | - |
dc.description.abstract | Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a Multi-Cell architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy. Further, considering the different stages of DR actually progress gradually, which means the labels of different stages are related. To considering the relationships of images with different stages, we propose a Multi-Task learning strategy which predicts the label with both classification and regression. Experimental results on the Kaggle dataset show that our method achieves a Kappa of 0.841 on test set which is the 4th rank of all state-of-the-arts methods. Further, our Multi-Cell Multi-Task Convolutional Neural Networks (M2CNN) solution is a general framework, which can be readily integrated with many other deep neural network architectures. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | - |
dc.subject | Deep Learning | - |
dc.subject | Diabetic Retinopathy | - |
dc.subject | Medical Image | - |
dc.subject | Multi-Cell Architecture | - |
dc.subject | MultiTask Learning | - |
dc.title | Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/EMBC.2018.8512828 | - |
dc.identifier.pmid | 30440966 | - |
dc.identifier.scopus | eid_2-s2.0-85056640060 | - |
dc.identifier.volume | 2018-July | - |
dc.identifier.spage | 2724 | - |
dc.identifier.epage | 2727 | - |