Image Embedded Segmentation: Uniting Supervised and Unsupervised Objectives for Segmenting Histopathological Images

• URL: http://arxiv.org/abs/2001.11202v3
• Date: Wed, 25 Nov 2020 11:18:20 GMT
• Title: Image Embedded Segmentation: Uniting Supervised and Unsupervised Objectives for Segmenting Histopathological Images
• Authors: C. T. Sari, C. Sokmensuer, and C. Gunduz-Demir
• Abstract summary: This paper presents a new regularization method to train a fully convolutional network for semantic tissue segmentation. It relies on the benefit of unsupervised learning, in the form of image reconstruction, for network training. Our experiments demonstrate that it leads to better segmentation results in these datasets, compared to its counterparts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a new regularization method to train a fully convolutional network for semantic tissue segmentation in histopathological images. This method relies on the benefit of unsupervised learning, in the form of image reconstruction, for network training. To this end, it puts forward an idea of defining a new embedding that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single one and proposes to learn this united task by a single generative model. This embedding generates an output image by superimposing an input image on its segmentation map. Then, the method learns to translate the input image to this embedded output image using a conditional generative adversarial network, which is known as quite effective for image-to-image translations. This proposal is different than the existing approach that uses image reconstruction for the same regularization purpose. The existing approach considers segmentation and image reconstruction as two separate tasks in a multi-task network, defines their losses independently, and combines them in a joint loss function. However, the definition of such a function requires externally determining right contributions of the supervised and unsupervised losses that yield balanced learning between the segmentation and image reconstruction tasks. The proposed approach provides an easier solution to this problem by uniting these two tasks into a single one, which intrinsically combines their losses. We test our approach on three datasets of histopathological images. Our experiments demonstrate that it leads to better segmentation results in these datasets, compared to its counterparts.

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