dc.contributor.advisor | Ji, Jim Xiuquan | |
dc.creator | Li, Weizhi | |
dc.date.accessioned | 2018-09-21T15:53:21Z | |
dc.date.available | 2018-09-21T15:53:21Z | |
dc.date.created | 2017-12 | |
dc.date.issued | 2017-12-11 | |
dc.date.submitted | December 2017 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/169641 | |
dc.description.abstract | Developing an effective algorithm based on the handcrafted features from histological
images (histo-images) is difficult due to the complexity of histo-images. Deep network
models have achieved promising performances, as it is capable of capturing high-level features.
However, a major hurdle hindering the application of deep learning in histo-image
segmentation is to obtain large ground-truth data for training. Taking the segmentations
from simple off-the-shelf algorithms as training data will be a new way to address this hurdle.
The output from the off-the-shelf segmentations is considered to be noisy data, which
requires a new learning scheme for deep learning segmentation. Existing works on noisy
label deep learning are largely for image classification. In this thesis, we study whether
and how integrating imperfect or noisy “ground-truth” from off-the-shelf segmentation algorithms
may help achieve better performance so that the deep learning can be applied to
histo-image segmentation with the manageable effort.
Two noise-tolerant deep learning architectures are proposed in this thesis. One is based
on the Noisy at Random (NAR) Model, and the other is based on the Noisy Not at Random
(NNAR) Model. The largest difference between the two is that NNAR based architecture
assumes the label noise is dependent on features of the image. Unlike most existing works,
we study how to integrate multiple types of noisy data into one specific model. The proposed
method has extensive application when segmentations from multiple off-the-shelf
algorithms are available. The implementation of the NNAR based architecture demonstrates
its effectiveness and superiority over off-the-shelf and other existing deep-learningbased
image segmentation algorithms. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Histo-image segmentation | en |
dc.subject | deep learning | en |
dc.subject | noisy labels learning | en |
dc.title | Noise-Tolerant Deep Learning for Histopathological Image Segmentation | en |
dc.type | Thesis | en |
thesis.degree.department | Electrical and Computer Engineering | en |
thesis.degree.discipline | Electrical Engineering | en |
thesis.degree.grantor | Texas A & M University | en |
thesis.degree.name | Master of Science | en |
thesis.degree.level | Masters | en |
dc.contributor.committeeMember | Qian, Xiaoning | |
dc.contributor.committeeMember | Choe, Yoonsuck | |
dc.contributor.committeeMember | Duffield, Nick | |
dc.type.material | text | en |
dc.date.updated | 2018-09-21T15:53:22Z | |
local.etdauthor.orcid | 0000-0001-9437-4158 | |