Deep interactive learning: An efficient labeling approach for deep learning-based osteosarcoma treatment response assessment Conference Paper
| Authors: | Ho, D. J.; Agaram, N. P.; Schüffler, P. J.; Vanderbilt, C. M.; Jean, M. H.; Hameed, M. R.; Fuchs, T. J. |
| Title: | Deep interactive learning: An efficient labeling approach for deep learning-based osteosarcoma treatment response assessment |
| Conference Title: | 23rd International Conference of Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) |
| Abstract: | Osteosarcoma is the most common malignant primary bone tumor. Standard treatment includes pre-operative chemotherapy followed by surgical resection. The response to treatment as measured by ratio of necrotic tumor area to overall tumor area is a known prognostic factor for overall survival. This assessment is currently done manually by pathologists by looking at glass slides under the microscope which may not be reproducible due to its subjective nature. Convolutional neural networks (CNNs) can be used for automated segmentation of viable and necrotic tumor on osteosarcoma whole slide images. One bottleneck for supervised learning is that large amounts of accurate annotations are required for training which is a time-consuming and expensive process. In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs. After an initial labeling step is done, annotators only need to correct mislabeled regions from previous segmentation predictions to improve the CNN model until the satisfactory predictions are achieved. Our experiments show that our CNN model trained by only 7 h of annotation using DIaL can successfully estimate ratios of necrosis within expected inter-observer variation rate for non-standardized manual surgical pathology task. © 2020, Springer Nature Switzerland AG. |
| Keywords: | osteosarcoma; chemotherapy; cell death; medical imaging; tumors; prognostic factors; surgery; surgical resection; medical computing; educational technology; image segmentation; learning systems; primary bone tumors; automated segmentation; deep learning; whole slide images; computational pathology; convolutional neural networks; interactive learning; observer variations; satisfactory predictions |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 12265 |
| Conference Dates: | 2020 Oct 4-8 |
| Conference Location: | Lima, Peru |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 2020-01-01 |
| Start Page: | 540 |
| End Page: | 549 |
| Language: | English |
| DOI: | 10.1007/978-3-030-59722-1_52 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference Paper -- Export Date: 2 November 2020 -- Source: Scopus |
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12Ho -
10Jean
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