A Unified Software Package for Cancer Diagnosis
This research is supported by the National Cancer Institute grant U01 CA231782, PI: Linda G. Shapiro, co-PI: Joann G. Elmore
The long-term goal of this project is to develop a unified software package for sharing image analysis and machine learning tools to improve the accuracy and efficiency of cancer diagnosis, thus aiding in improving the quality of both cancer research and clinical practice. Our specific aims are as follows: 1. Regions of Interest: Produce 1a) a ROI-finder classifier and associated tools for use by researchers or pathologists for automatic identification of potential ROIs on whole slide images of breast biopsy slides and 1b) a ROI-analysis classifier and associated tools that can point out image regions that tend to cause misdiagnosis and produce suitable warnings as to why such regions may either be distractors or indicate cancer; 2. Diagnosis: Produce a diagnostic classifier and associated tools that can not only suggest the potential diagnosis of a whole slide image, but can also produce the reasons for the diagnosis in terms of regions on the image, their color, their texture, and their structure; 3. Dissemination: Develop a unified software package containing this suite of tools, so they can be easily shared and provided (standalone and through the existing Pathology Image Informatics Platform (PIIP)) to both cancer researchers and clinical pathologists. In addition to specific classifiers for breast cancer research, we will provide the methodology to train related classifiers for other biopsy-diagnosed cancers, such as melanoma, prostate, lung, and colon cancer.
People
| Linda Shapiro |
Joann Elmore |
Donald Weaver |
Meredith Wu |
Beibin Li |
Kechun Liu |
Shima Nofallah |
 |
 |
 |
 |
 |
 |
 |
Publications
[1] Wu, W.; Li, B.; Mercan, E..; Mehta, S.; Bartlett, J.; Weaver, D.; Elmore, J.; Shapiro, L. MLCD: A Unified Software Package for Cancer Diagnosis In Journal of Clinical Oncology (JCO). 2020 [Link], [PDF]
[2] Li, B.; Mercan, E.; Mehta, S.; Knezevich, S.; Arnold, C.W.; Weaver, D.L.; Elmore, J.G.; Shapiro, L.G. Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline. In 2020 IEEE International Conference on Pattern Recognition (ICPR). IEEE. 2020. [Paper]
Presentations
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline. ICPR 2020. [Video]