Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Identification of invasive cancer in Whole Slide Images (WSIs) is crucial for tumor staging as well as treatment planning. However, the precise manual delineation of tumor regions is challenging, tedious and time-consuming. Thus, automatic invasive cancer detection in WSIs is of significant importance. Recently, Convolutional Neural Network (CNN) based approaches advanced invasive cancer detection. However, computation burdens of these approaches become barriers in clinical applications. In this work, we propose to detect invasive cancer employing a lightweight network in a fully convolution fashion without model ensembles. In order to improve the small network’s detection accuracy, we utilized the “soft labels” of a large capacity network to supervise its training process. Additionally, we adopt a teacher guided loss to help the small network better learn from the intermediate layers of the high capacity network. With this suite of approaches, our network is extremely efficient as well as accurate. The proposed method is validated on two large scale WSI datasets. Our approach is performed in an average time of 0.6 and 3.6 min per WSI with a single GPU on our gastric cancer dataset and CAMELYON16, respectively, about 5 times faster than Google Inception V3. We achieved an average FROC of \(81.1\%\) and \(85.6\%\) respectively, which are on par with Google Inception V3. The proposed method requires less high performance computing resources than state-of-the-art methods, which makes the invasive cancer diagnosis more applicable in the clinical usage.


Invasive Cancer Detection Transfer Learning Large Network Capacity Whole Slide Images (WSI) Gastric Cancer Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is partially supported by the National Science Foundation under grant IIP-1439695, ABI-1661280 and CNS-1629913.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUNC CharlotteCharlotteUSA
  2. 2.CuraCloud CorporationSeattleUSA

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