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Hirsch, Jan-Michael
Publications (2 of 2) Show all publications
Wieslander, H., Forslid, G., Bengtsson, E., Wählby, C., Hirsch, J.-M., Runow Stark, C. & Kecheril Sadanandan, S. (2017). Deep convolutional neural networks for detecting cellular changes due to malignancy. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW 2017): . Paper presented at ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017. (pp. 82-89). IEEE
Open this publication in new window or tab >>Deep convolutional neural networks for detecting cellular changes due to malignancy
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2017 (English)In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW 2017), IEEE, 2017, p. 82-89Conference paper, Published paper (Refereed)
Abstract [en]

Discovering cancer at an early stage is an effective way to increase the chance of survival. However, since most screening processes are done manually it is time inefficient and thus a costly process. One way of automizing the screening process could be to classify cells using Convolutional Neural Networks. Convolutional Neural Networks have been proven to be accurate for image classification tasks. Two datasets containing oral cells and two datasets containing cervical cells were used. For the cervical cancer dataset the cells were classified by medical experts as normal or abnormal. For the oral cell dataset we only used the diagnosis of the patient. All cells obtained from a patient with malignancy were thus considered malignant even though most of them looked normal. The performance was evaluated for two different network architectures, ResNet and VGG. For the oral datasets the accuracy varied between 78-82% correctly classified cells depending on the dataset and network. For the cervical datasets the accuracy varied between 84-86% correctly classified cells depending on the dataset and network. The results indicate a high potential for detecting abnormalities in oral cavity and in uterine cervix. ResNet was shown to be the preferable network, with a higher accuracy and a smaller standard deviation.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE International Conference on Computer Vision Workshops, E-ISSN 2473-9936
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329356 (URN)10.1109/ICCVW.2017.18 (DOI)000425239600011 ()978-1-5386-1034-3 (ISBN)
Conference
ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017.
Funder
EU, European Research Council, 682810Swedish Research Council, 2012-4968
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-05-23Bibliographically approved
Nowinski, D., Messo, E., Hedlund, A. & Hirsch, J.-M. (2011). Computer-navigated contouring of craniofacial fibrous dysplasia involving the orbit. The Journal of craniofacial surgery (Print), 22(2), 469-472
Open this publication in new window or tab >>Computer-navigated contouring of craniofacial fibrous dysplasia involving the orbit
2011 (English)In: The Journal of craniofacial surgery (Print), ISSN 1049-2275, E-ISSN 1536-3732, Vol. 22, no 2, p. 469-472Article in journal (Refereed) Published
Abstract [en]

Virtual surgical planning and computer-aided surgery were used to treat a mono-ostotic fibrous dysplasia of the right zygoma. Mirroring of the contralateral zygoma sets the target for the contouring of the affected zygomatic bone. An optical system for computer-guided surgery was used. Instruments were calibrated and visualized in real time on screen. Achievement of the virtually set target for the orbitozygomatic anatomy was assessed during surgery. Postoperative computed tomography and clinical follow-up confirmed an excellent result with regard to facial symmetry and eye bulb position. The volume of the orbit was increased from 24.2 to 26.0 mL compared with a contralateral orbital volume of 25.7 mL. Computer-guided surgery may be a useful tool in the surgical reduction of craniofacial fibrous dysplasia.

Keyword
Computer-assisted surgery, orbit, virtual planning
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-151519 (URN)10.1097/SCS.0b013e3182074312 (DOI)000288535800022 ()21403578 (PubMedID)
Available from: 2011-04-13 Created: 2011-04-13 Last updated: 2017-12-11Bibliographically approved
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