Detecting basal cell carcinoma in skin histopathological images using deep learning
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/121624 |
| _version_ | 1826204235195219968 |
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| author | Calderón Gómez, Tomás Alberto. |
| author2 | Tomaso Poggio. |
| author_facet | Tomaso Poggio. Calderón Gómez, Tomás Alberto. |
| author_sort | Calderón Gómez, Tomás Alberto. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T12:51:03Z |
| format | Thesis |
| id | mit-1721.1/121624 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:51:03Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1216242019-09-20T03:03:17Z Detecting basal cell carcinoma in skin histopathological images using deep learning Calderón Gómez, Tomás Alberto. Tomaso Poggio. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 51). In this thesis we explore different machine learning techniques that are common in image classification to detect the presence of Basal Cell Carcinoma (BCC) in digital skin histological images. Since digital histology images are extremely large, we first focused on determining the presence of BCC at the patch level, using pre-trained deep convolutional neural networks as feature extractors to compensate for the size of our datasets. The experimental results show that our patch level classifiers obtained an area under the receiver operating characteristic curve (AUC) of 0.981. Finally, we used our patch classifiers to generate a bag of scores for a given whole slide image (WSI), and attempted multiple ways of combining these scores to produce a single significant score to predict the presence of BCC in the given WSI. Our best performing model obtained an AUC of 0.991 in 86 samples of digital skin biopsies, 43 of which had BCC. by Tomás Alberto Calderón Gómez. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-07-15T20:28:54Z 2019-07-15T20:28:54Z 2018 2018 Thesis https://hdl.handle.net/1721.1/121624 1098171611 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 51 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Calderón Gómez, Tomás Alberto. Detecting basal cell carcinoma in skin histopathological images using deep learning |
| title | Detecting basal cell carcinoma in skin histopathological images using deep learning |
| title_full | Detecting basal cell carcinoma in skin histopathological images using deep learning |
| title_fullStr | Detecting basal cell carcinoma in skin histopathological images using deep learning |
| title_full_unstemmed | Detecting basal cell carcinoma in skin histopathological images using deep learning |
| title_short | Detecting basal cell carcinoma in skin histopathological images using deep learning |
| title_sort | detecting basal cell carcinoma in skin histopathological images using deep learning |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/121624 |
| work_keys_str_mv | AT calderongomeztomasalberto detectingbasalcellcarcinomainskinhistopathologicalimagesusingdeeplearning |