Deep convolutional network for tumor bud detection
Author(s)
Advisor
Demir, Çiğdem GündüzDate
2019-04Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
The existence of tumor buds is accepted as a promising biomarker for staging
colorectal carcinomas. In the current practice of medicine, these tumor buds are
detected by the manual examination of a immunohistochemically (IHC) stained
tissue sample under a microscope. This manual examination is time-consuming
as well as it may lead to inter-observer variability. In order to obtain fast and reproducible
examinations, developing computational solutions has been becoming
more and more important. With this motivation, this thesis presents a fully convolutional
network design for the purpose of automatic tumor bud detection, for
the rst time. This network design extends the U-net architecture by considering
up-to-date learning mechanisms. These mechanisms include using residual connections
in the encoder path, employing both ELU and ReLU activation functions
in di erent layers of the network, training the network with a Tversky loss function,
and combining outputs of di erent layers of the decoder path to reconstruct
the nal segmentation map. Our experiments on 3295 image tiles taken from 23
whole slide images of IHC stained colorectal carcinomatous samples show that
this extended version helps alleviate the vanishing gradient problem and those
related with having a high class-imbalance dataset. And as a result, this network
design yields better segmentation results compared with those of the two
state-of-the-art networks.
Keywords
Deep learningFully convolutional networks
Digital pathology
Tumor budding
Colorectal carcinomas