Boosting fully convolutional networks for gland instance segmentation in histopathological images
Author(s)
Advisor
Demir, Çiğdem GündüzDate
2019-08Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
In the current literature, fully convolutional neural networks (FCNs) are the most
preferred architectures for dense prediction tasks, including gland segmentation.
However, a signi cant challenge is to adequately train these networks to correctly
predict pixels that are hard-to-learn. Without additional strategies developed for
this purpose, networks tend to learn poor generalizations of the dataset since the
loss functions of the networks during training may be dominated by the most
common and easy-to-learn pixels in the dataset. A typical example of this is the
border separation problem in the gland instance segmentation task. Glands can
be very close to each other, and since the border regions contain relatively few
pixels, it is more di cult to learn these regions and separate gland instances. As
this separation is essential for the gland instance segmentation task, this situation
arises major drawbacks on the results. To address this border separation problem,
it has been proposed to increase the given attention to border pixels during
network training either by increasing the relative loss contribution of these pixels
or by adding border detection as an additional task to the architecture. Although
these techniques may help better separate gland borders, there may exist other
types of hard-to-learn pixels (and thus, other mistake types), mostly related to
noise and artifacts in the images. Yet, explicitly adjusting the appropriate attention
to train the networks against every type of mistake is not feasible. Motivated
by this, as a more e ective solution, this thesis proposes an iterative attention
learning model based on adaptive boosting. The proposed AttentionBoost model
is a multi-stage dense segmentation network trained directly on image data without
making any prior assumption. During the end-to-end training of this network,
each stage adjusts the importance of each pixel-wise prediction for each image
depending on the errors of the previous stages. This way, each stage learns the
task with di erent attention forcing the stage to learn the mistakes of the earlier stages. With experiments on the gland instance segmentation task, we demonstrate
that our model achieves better segmentation results than the approaches
in the literature.
Keywords
Deep learningAttention learning
Adaptive boosting
Gland segmentation
Medical image segmentation