SOURCES SOUGHT
A -- Deep Learning for Digitized Dermoscopy Lesion Classification and Development of Publicly Available Dermatology Image Collection - Draft SOW
- Notice Date
- 7/24/2018
- Notice Type
- Sources Sought
- NAICS
- 54171
— Research and Development in the Physical, Engineering, and Life SciencesT
- Contracting Office
- Department of Health and Human Services, National Institutes of Health, National Library of Medicine, 6707 Democracy Blvd., Suite 105, Bethesda, Maryland, 20894, United States
- ZIP Code
- 20894
- Solicitation Number
- NIHLM201800084
- Archive Date
- 8/15/2018
- Point of Contact
- Keturah D. Busey, Phone: 3014966546
- E-Mail Address
-
buseyk@mail.nlm.nih.gov
(buseyk@mail.nlm.nih.gov)
- Small Business Set-Aside
- N/A
- Description
- Draft SOW This is a Small Business Sources Sought Notice. This is NOT a solicitation for proposals, proposal abstracts, or quotations. The purpose of this notice is to obtain information regarding: (1) the availability and capability of qualified small business sources; (2) whether they are small businesses; HUBZone small businesses; service- disabled, veteran-owned small businesses; 8(a) small businesses; veteran-owned small businesses; woman-owned small businesses; or small disadvantaged businesses; and (3) their size classification relative to the North American Industry Classification System (NAICS) code for the proposed acquisition. Your responses to the information requested will assist the Government in determining the appropriate acquisition method, including whether a set-aside is possible. An organization that is not considered a small business under the applicable NAICS code should not submit a response to this notice. The National Institutes of Health (NIH), National Library of Medicine (NLM) is conducting a market survey to help determine the availability and technical capability of qualified small businesses, veteran-owned small businesses and/or HUBZone small businesses capable of serving the needs identified below. This Sources Sought Notice is for informational and planning purposes only and shall not be construed as a solicitation or as an obligation or commitment by the Government. This notice is intended strictly for market research. BACKGROUND The title of the Strategic Plan of the National Library of Medicine is Biomedical Discovery and Data-powered Health. An important kind of health data is medical imaging data. A key challenge in clinical decision making using the imaging data is the fusion of medical metadata, i.e., the patient's medical information. Recently, Deep learning (DL) image processing techniques such as convolutional neural networks (CNNs) have enabled automatic image classification on par with experts. The impact of these techniques can be increased by adding a more relevant viewpoints-that of the patient and the clinician. Fusion of such non-image clinical information and the biomedical image information can help bring the vision in the Strategic Plan closer to reality. The domain of dermatology oncology is a good candidate for this effort because of 1) the importance of early detection; 2) availability of large numbers of images with correlated clinical information; and, 3) the need for tools available for mid-level providers and even patients themselves, who increasingly have access to the requisite imaging devices. Better search and image-processing tools have brought diagnosis into the hands of the non-expert. Non-specialists, mid-level clinician practitioners and even patients are on the verge of being able to reliably diagnose visible lesions themselves. The "gap", however, is the subjective variability in the expertise of the mid-level clinical practitioners and the lack of an automated artificial intelligence powered device that help the patient make better decisions regarding level of needed care. There is a need for research that builds on recent developments in image processing technology that afford high accuracy in diagnosis, both at the intermediate diagnostic feature level and the final diagnosis level. Both results are needed to make the best clinical decision and to provide a sound and supportable basis for the decision. Recent work shows that surprisingly good diagnostic accuracy using deep learning can be derived from clinical images alone (Dermatologist-level classification of skin cancer with deep neural networks. Esteva et al., Nature, 542:115-118, February 2017); and, 2) increased diagnostic accuracy is achieved by fusing patient's clinical information with their dermoscopy image information (Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Haenssle et al. Annals of Oncology, May 2018). Unfortunately, much recent work by those with DL expertise, has bypassed clinically and physiologically relevant information, to the detriment of those making the decision for the best clinical path. Further, much of the DL and CNN work is seen as "black-box" with unexplained information on how the algorithm arrived at a decision, what features led to the decision, and their relevance to physiology. There is an opportunity to include maximum relevant information to provide a decision for the best clinical path. Combining the decision along with an atlas allows for the superior human analog / comparison processing to participate in the decision. OBJECTIVE Professional services are required to (1) develop and evaluate hybrid imaging and deep learning techniques for development of skin lesion border detection, feature development, and classification; and, (2) development and acquisition of a high-quality dermatology image archive (20,000 images) with attached metadata for training, testing, and validation the classifier. The work will target key features critical for detecting different lesion types/classes. PROJECT REQUIREMENTS See attached draft statement of work. Anticipated Period of Performance: The period of performance will be for base period from September 24, 2018 through September 23, 2019 with one (1) one-year option period. Other Important Considerations This Sources Sought Notice is not a Request for Quotes (RFQ), nor is an RFQ available. All responsible sources may submit a capability statement which will be considered by the National Library of Medicine. Interested firms responding to this Sources Sought Notice must adhere to the following: (a) Provide a capability statement demonstrating relevant experience, skills and ability to fulfill the Government's requirements for the above. The capability statement should contain enough sufficient detail for the Government to make an informed decision regarding your capabilities; however, the statement should not exceed 10 pages. (b) The capability statement must identify the responder's: small business type and size; DUNS number; NAICS code; and technical and administrative points of contact, including names, titles, addresses, telephone and fax numbers, and e-mail addresses. (c) All capability statements must be submitted electronically no later than 12:00 p.m. local prevailing time on Tuesday, July 31, 2018. NLM requires capability statements to be submitted electronically through the NLM Electronic Contract Proposal System (eCPS) using the following link: https://ecps.nih.gov All capability statements received via eCPS by Tuesday, July 31, 2018 at 12:00 p.m. local prevailing time will be considered by NLM. For directions on using eCPS, go to https://ecps.nih.gov and click on "How to Submit." NOTE: To submit your electronic capability statement using eCPS, all offerors must have a valid NIH External Directory Account, which provides authentication and serves as a vehicle for secure transmission of documents and communication with the NLM. The NIH External Directory Account registration process may take up to 24 hours to become active. Submission of proposals by facsimile or e-mail is not accepted. Firms interested in responding to this notice must be able to provide the professional services required by NLM outlined in the attached Statement of Work. The offeror shall include all information necessary to document and/or support the qualification criteria in one clearly marked section of its proposal. All responses from responsible sources will be considered. Disclaimer and Important Notes: This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government reserves the right to use information provided by respondents for any purpose deemed necessary and legally appropriate. Any organization responding to this notice should ensure that its response is complete and sufficiently detailed to allow the Government to determine the organization's qualifications to perform the work. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. After a review of the responses received, a pre-solicitation synopsis and solicitation may be published in Federal Business Opportunities. However, responses to this notice will not be considered adequate responses to a solicitation. Confidentiality: No proprietary, classified, confidential, or sensitive information should be included in your response. The Government reserves the right to use any non-proprietary technical information in any resultant solicitation(s).
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- Record
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