Jacobs, JG; Brostow, GJ; Freeman, A; Alexander, DC; Panagiotaki, E; (2017) Detecting and Classifying Nuclei on a Budget. In: Cardoso, MJ and Arbel, T and Lee, SL and Cheplygina, V and Balocco, S and Mateus, D and Zahnd, G and Maier-Hein, L and Demirci, S and Granger, E and Duong, L and Carbonneau, MA and Albarqouni, S and Carneiro, G, (eds.) LABELS 2017, CVII 2017, STENT 2017: Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. (pp. pp. 77-86). Springer International Publishing: Cham, Switzerland.

Preview

Text LABELS2017_7.pdf - Accepted Version Download (2MB) | Preview

Abstract

The benefits of deep neural networks can be hard to realise in medical imaging tasks because training sample sizes are often modest. Pre-training on large data sets and subsequent transfer learning to specific tasks with limited labelled training data has proved a successful strategy in other domains. Here, we implement and test this idea for detecting and classifying nuclei in histology, important tasks that enable quantifiable characterisation of prostate cancer. We pre-train a convolutional neural network for nucleus detection on a large colon histology dataset, and examine the effects of fine-tuning this network with different amounts of prostate histology data. Results show promise for clinical translation. However, we find that transfer learning is not always a viable option when training deep neural networks for nucleus classification. As such, we also demonstrate that semi-supervised ladder networks are a suitable alternative for learning a nucleus classifier with limited data.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item