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Detecting and Classifying Nuclei on a Budget

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. Green open access

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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.

Type: Proceedings paper
Title: Detecting and Classifying Nuclei on a Budget
Event: 6th Joint International Workshops, CVII-STENT 2017 and Second International Workshop, LABELS 2017, Held in Conjunction with MICCAI 2017
ISBN-13: 9783319675336
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-67534-3_9
Publisher version: http://dx.doi.org/10.1007/978-3-319-67534-3_9
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1573202
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