|
In today’s clinical routine, malignant changes in the prostate’s structure are evaluated based on the Gleason grading system. An inevitable variability of the histopathological grading and thus a lack of reproducibility have led to a wide variety of computer-aided approaches including form-descriptor and deep learning based solutions. A common problem for any of those approaches lies in the domain-specific conditions, which consist in the small size of datasets, the high resolution and stain-fluctuation of the images, the missing capability of including useful follow-up data as well as the lack of region-level annotations and a high label-noise. Hence, in this work we analyze how the advantages of deep learning can be used for the malignancy grading of histopathological whole-slide images of the prostate, without relying too much on annotations and large-scale datasets. Our novel framework combines a clustering based stain normalization with a chain of an autoencoder, a principal component analysis, a support vector machine, and further deep learning and statistical models. Using this approach the classification accuracy could be improved by 5–10% when compared to conventional approaches. In addition, first results in combining biochemical and follow-up data with images for the prediction of the biochemical relapse time could be shown. The proposed framework has been built and validated using 500 cases taken from The Cancer Genome Atlas (TCGA: https://www.cancer.gov/tcga). Our work presents novel and promising approaches for the assessment of the prostate carcinoma’s malignancy and the severity of the risk using deep learning, as well as pending problems and limitations.
|
