2026-09-18T20:14:55Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1216242026-06-06T00:55:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomaso Poggio.
Calderón Gómez, Tomás Alberto.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-15T20:28:54Z
2019-07-15T20:28:54Z
2018
2018
https://hdl.handle.net/1721.1/121624
1098171611
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
51 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Detecting basal cell carcinoma in skin histopathological images using deep learning
Thesis
application/pdf
2019-07-15T20:28:51Z
Publication
Master
EECS
unknown
unknown
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<Abstract>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.</Abstract>
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