Kiyani, Dr. Amber and Aqeel, Dr. Hassan and Tanveer, Mr. Asjid and Nawaz, Mr. Wajahat and Khurram, Dr. Syed Ali (2019) APPLICATION OF DEEP LEARNING ALGORITHMS IN DETECTION OF MITOTIC EVENTS IN ORAL SQUAMOUS CELL CARCINOMA USING CELLPHONE IMAGES. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 128 (1). e79-e80. ISSN 22124403
Full text not available from this repository.Abstract
Identifying mitoses in tumors and metastatic deposits in lymph nodes can be laborious and time-consuming tasks. Advances in digital pathology and machine learning algorithms have demonstrated promising results by automating these assignments in breast tissue and sentinel lymph node sections. These breakthroughs have made automated histopathological diagnosis a possibility. All prior studies have used high-resolution images from expensive whole slide image (WSI) scanners for training and detection of cellular events. Our aim was to investigate the efficacy of deep learning algorithms for automated detection of mitotic events on low quality images of oral squamous cell carcinoma (OSCC) produced by cellphone cameras.
Methodology
A FAST region-based convoluted neural network was trained on WSI from breast cancer. The mitotic events were highlighted through provision of pixel locations to the training algorithm, each patch was approximately 301 × 301 in size. The non-mitosis regions were randomly selected on the images. The final training data set comprised of 4407 image patches. Transfer learning was applied to generate results. Similar algorithms were employed on a data set of comparable size acquired through a cellphone camera from 13 different OSCCs at high-power (40x).
Results
The WSI demonstrated true positive rates of 0.46 and a false positive of 0.76 with an overall F1 precision of 0.57. The results from cellphone camera showed true positive rates of 0.46, and false positive rates of 0.54. The overall F1 score was 0.49.
Conclusion
Although WSIs outperformed cellphone images in identifying mitoses, enhancing image quality through modified algorithms may improve efficacy. This will facilitate use of low-cost data sets for training future algorithms for automated detection of cellular events, and widen its impact by making it accessible to every pathologist with a cellphone camera.
| Item Type: | Article |
|---|---|
| Subjects: | R Medicine > RK Dentistry |
| Divisions: | Faculty of Health and Medical Sciences (FHMS) > Islamic International Dental College Islamabad |
| Depositing User: | Dr Kanwal Sohail |
| Date Deposited: | 09 Mar 2020 08:44 |
| Last Modified: | 09 Mar 2020 08:44 |
| URI: | http://research.riphah.edu.pk/id/eprint/276 |
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