The problem we solve: : Machine learning image analysis requires defining ground truth; a training set is necessary for the deep learning algorithms to learn. There is known variation between experts, with reported inter-observer variability in Radiology and Pathology as high as 15% and 12%, respectively. Variability has prompted institutions to require double reading of images, increasing workload and time. Computer aided diagnosis (CAD) can act as an automated “second check”, reducing the cases needed for review. However, in order to develop accurate CAD, machine learning algorithms need well annotated training sets. The small number of subject matter experts (SMEs) training today’s systems magnifies the issue of variation, and leads to inaccuracies in the “source of truth,” dependent on the expertise of the clinicians who have trained the system. Creating a simpler way for SMEs to annotate images would expand the pool of experts and lead to crowd-sourcing of the training sets.
About our solution: Captcha is an open-source security system intended to protect websites by distinguishing between human and machine input. There are several image-based Captchas, such as ESP-PIX and SQ-PIX, which allows users to identify individual parts in an image We propose to utilize the Captcha technology to enable crowd-sourcing of image training sets. Users can quickly identify the areas of interest using the captcha technology. With ESP-PIX, users can identify simple objects such as a heart on an x-ray by choosing the portions of the image that contain the object of interest. In contrast, SQ-PIX allows users to trace a complex object, such as invasive carcinoma on a whole slide image. These techniques can be applied to large image data repositories across multiple institutions, and would enable and encourage crowd-sourcing of the training sets. This would lead to faster validation of much larger data sets, as well as reduce the inherent variability when using small numbers of SMEs.Progress to date:
This project is early in its conception phase, and we are working on a plan for rapid innovative cycles for its development and testing. There is support for this effort within the informatics, pathology and radiology communities at our institution. Geisinger has a unified data architecture (UDA) which is able to ingest a large volume of healthcare images, as well as a Hadoop platform for data warehousing. Currently we have an image repository which contains millions of healthcare images, a portion of which could be annotated as a test case for machine learning techniques using crowd-sourced image-identification techniques. The test case will ask how can HI-Captcha be applied, whether experts easily use it, and do they like using it We believe that utilizing HI-Captcha has the potential to provide relevant images for providers who need verification, as well as rapidly annotate large repositories of healthcare image for eventual evaluation by different machine learning algorithms.
Creator: Chancey Christenson
Education: Tulane University Medical School
Bio: I am a 2nd year Clinical Inforamtics Fellow at Geisinger Medical Center and am interested in Machine learning techniques. I am a Board certified Clinical Pathologist from Tulane University with a Fellowship in Transfusion Medicine from New York Blood Center. Figuring out ways to better teach computers to aid in Computer Assisted Diagnosis is a major goal of mine.
Hospital Affiliation: Geisinger Medical Center
Title: Clinical Informatics Fellow
Advanced Degree(s): MD,MPH
As patients, we all want accurate and timely diagnoses, which can guide our health care team to deliver the best care. This basic truth is valid for many diseases and conditions, whether relatively simple or complex. There is an ever-increasing cognitive workload placed upon clinicians, who are asked to see more patients and make more diagnoses in a shorter amount of time. Increasing this workload imposes a human cost in terms of physician fatigue, and represents a potential source of missed or improper diagnosis. Tools that support the clinician by leveraging the power of image analysis and machine learning will provide increased diagnostic accuracy and decreased variability in care Instances of missed, incorrect or incomplete diagnoses will decrease. Patients will benefit from these timely and more accurate diagnoses through more focused treatment options provided more quickly.
A well-developed CAD image analysis system can both prescreen cases to focus the clinician on the diagnostic features of the image, as well as act as an automated second opinion for a clinician, leading to a reduction in both physician workload and diagnostic errors. Perceptual errors occur when an important diagnostic aspect in an image is missed, whether through fatigue and/or lack of experience or knowledge. However, the accuracy and effectiveness of CAD systems require well annotated training sets. Utilizing HI-Captcha with medical images would facilitae the creation and annotation of these training sets through inter-institutional expert crowd sourcing. With Captcha we can have 1000 experts annotate each image, and these images can be aggregated from multiple institutions across the world anonymously to protect patient privacy. This will accelerate the use of automated image analysis of medical images, leading to a reduction in clinician workload, and reducing the frequency of diagnostic errors. This benefits not only the clinicians performing the diagnosis of medical images, but also assists treating clinicans wtith more accurate and timely diagnoses on which to base their treatment decisions.
Hospitals are increasingly focused on providing the highest quality and efficiency of care. In addition, numerous studies examining decades of malpractice litigation in radiology and pathology demonstrate that the overwhelming majority of these cases involve alleged diagnostic mistakes attributed to perceptual errors and errors in judgement. Image analysis systems that provide diagnostic support to providers can improve diagnostic accuracy and the timeliness of diagnoses while reducing the burden on these diagnostic providers as well as reduce the incidence of malpractice cases related to diagnostic errors. HI-Captcha would facilitate inter-institutional collaboration of images and experts to create the most comprehensive and accurate training sets for computerized image analysis systems. In addition, many current image analysis systems are proprietary, and relatively expensive to implement. Our HI-Captcha solution is based on open-source technology that would lead to reduced costs.
There is a tremendous push towards interinstitutional collaboration, especially in the medical research community as well as in the development of new technologies. While large integrated health delivery networks can leverage their size and diversity, smaller health systems struggle in this area. A simple and open source tool such as Captcha image analysis facilitates collaboration between health systems for everyone’s mutual benefit. For example, many institutions have developed biorepositories for research, but it is difficult to identify appropriate specimens for a specific research project, especially across institutions. Better image analysis through Captcha can allow researchers to quickly locate appropriate specimens for study across multiple institutions. Entrepreneurs and start-up companies are constantly searching for health care partners to assist in the development of a concept or product. Captcha image analysis will enable those with a new idea involving the use of images to quickly and easily test their concept. There is also active collaboration amongst and between deep learning experts and healthcare, and this solution will accelerate the collaborations between these groups of experts.
Key Milestones Achieved and Planned
Geisinger has a unified data architecture and a Hadoop platform. This provides access to a well established repository of healthcare iamges. If successful with this pitch we propose to create a test case using 150 de-identified images which will be evaluated by a group of 15 physician informaticists and 10 nurse informaticists. We will assess ease of use and accuracy of annotation from the test case. The test case training images will be developed over the next quarter, with test case study to be implemented in the first quarter of 2018.
Our Competitive Advantages
Our product uses existing open source technology, but applies a novel application for healthcare annotation to it. This will enable crowd sourced annotation of large data sets of healthcare repositories. Thus, new stakeholder groups we would like to attract are entrepreneurs and start-up groups which would like access to already annotated training sets of healthcare images on which to test their machine learning algorithms.
Barriers to Entry
Having access to an established image repository for health care images along with acting in an early-mover position will help reduce barriers to market. Ultimately we hope to prove proof of concept and begin multi-institutional collaboration.
Traction, Funding and Partners
Currently Geisinger provides a stipend to the fellows for research. We hope to use the Amia Pitch competition to develop our test case, and from there, approach Big-Data investors such as Google to continue work on the crowd sourced annotation for image analysis.
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