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      Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images

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          Abstract

          Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target domain. The adaptation is achieved through adversarial training to find an invariant feature space along with the proposed Siamese architecture on the target domain to add a regularization that is appropriate for the whole-slide images. We validate the method on two prostate cancer datasets and obtain significant classification improvement of Gleason scores as compared with the baseline models.

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          Deep Learning in Neural Networks: An Overview

          (2014)
          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            Combining unsupervised feature learning and riesz wavelets for histopathology image representation: application to identifying anaplastic medulloblastoma

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              Author and article information

              Journal
              04 June 2018
              Article
              1806.01357
              e4312c89-5de6-43bb-b83b-e1cc950a6db7

              http://arxiv.org/licenses/nonexclusive-distrib/1.0/

              History
              Custom metadata
              Accepted to MICCAI 2018
              cs.CV

              Computer vision & Pattern recognition
              Computer vision & Pattern recognition

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