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.
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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.
| 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 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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