Deep Learning with a DAG Structure for Segmentation and Classification of Prostate Cancer
(2016) In Master's Theses in Mathematical Sciences FMA820 20161Mathematics (Faculty of Engineering)
- Abstract
- Deep learning is a machine learning technique inspired by the biological nervous system. The method has been used more and more over the last decades. Within this thesis, convolutional neural networks (CNN:s) are used for Gleason classification of prostate cancer in histopathological images. The data that has been used consists of digitalized microscopic images of prostate biopsies stained with haematoxylin-eosin (H&E). There are separate samples containing benign, Gleason 3, Gleason 4 and Gleason 5 tissue. More specifically, the potential of a directed acyclic graph (DAG) structure for the classifying CNN has been investigated. In this network a segmentation image has been used as a second label in the middle of the network, and the... (More)
- Deep learning is a machine learning technique inspired by the biological nervous system. The method has been used more and more over the last decades. Within this thesis, convolutional neural networks (CNN:s) are used for Gleason classification of prostate cancer in histopathological images. The data that has been used consists of digitalized microscopic images of prostate biopsies stained with haematoxylin-eosin (H&E). There are separate samples containing benign, Gleason 3, Gleason 4 and Gleason 5 tissue. More specifically, the potential of a directed acyclic graph (DAG) structure for the classifying CNN has been investigated. In this network a segmentation image has been used as a second label in the middle of the network, and the backpropagation has thus been performed using two different outputs. Hence, as a buildup, a CNN with a simple structure was created for segmentation of different components in benign prostate tissue. The structure and trained weights of this segmentation network were then used in the classifying DAG net. No final fine-tuning of the hyper-parameters or optimization of the network has been performed, but the cost function showed a decreasing trend when the training was carried out. Therefore it was concluded that the DAG structure does have potential and that further development of the method could yield a high performance network. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8890727
- author
- Flood, Gabrielle LU
- supervisor
-
- Karl Åström LU
- organization
- course
- FMA820 20161
- year
- 2016
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Deep learning, convolutional neural networks, CNN, directed acyclic graph, DAG, prostate cancer
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3304-2016
- ISSN
- 1404-6342
- other publication id
- 2016:E44
- language
- English
- id
- 8890727
- date added to LUP
- 2016-09-12 13:52:02
- date last changed
- 2016-09-12 13:52:02
@misc{8890727, abstract = {{Deep learning is a machine learning technique inspired by the biological nervous system. The method has been used more and more over the last decades. Within this thesis, convolutional neural networks (CNN:s) are used for Gleason classification of prostate cancer in histopathological images. The data that has been used consists of digitalized microscopic images of prostate biopsies stained with haematoxylin-eosin (H&E). There are separate samples containing benign, Gleason 3, Gleason 4 and Gleason 5 tissue. More specifically, the potential of a directed acyclic graph (DAG) structure for the classifying CNN has been investigated. In this network a segmentation image has been used as a second label in the middle of the network, and the backpropagation has thus been performed using two different outputs. Hence, as a buildup, a CNN with a simple structure was created for segmentation of different components in benign prostate tissue. The structure and trained weights of this segmentation network were then used in the classifying DAG net. No final fine-tuning of the hyper-parameters or optimization of the network has been performed, but the cost function showed a decreasing trend when the training was carried out. Therefore it was concluded that the DAG structure does have potential and that further development of the method could yield a high performance network.}}, author = {{Flood, Gabrielle}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Deep Learning with a DAG Structure for Segmentation and Classification of Prostate Cancer}}, year = {{2016}}, }