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4 results for “Hybrid intelligence”
Toward a Quality Model for Hybrid Intelligence Teams - Supplementary Material
<p>Supplementary material for paper:</p> <div><span>Dell’Anna D, Murukannaiah PK, Dudzik B, Grossi D, Jonker CM, Oertel C, et al.</span> <span>Toward a Qual</span><span>ity Model for Hybrid Intelligence Teams.</span> <span>In: Proceedings of the 23rd International Conference on </span><span>Autonomous Agents and MultiAgent Systems, AAMAS 2024<br><br></span> <div> </div> </div> <div> <div># GENERAL INFORMATION</div> <div> </div> <div> </div> <div>## Title</div> <div> </div> <div>Supplementary material for paper "Toward a Quality Model for Hybrid Intelligence Teams"</div> <div> </div> <div> </div> <div># FILE OVERVIEW</div> <div> </div> <div>```</div> <div>AAMAS2024_Toward_HI_Quality_Model_Supplementary_Material</div> <div>│ README.md</div> <div>│ Group Discussion - Handout - Essentials # The printed version of the questionnaires (Essentials) and team descriptions handed out to the groups for the group discussion</div> <div>│ Group Discussion - Handout - Enablers # The printed version of the questionnaires (Enablers, except Coaching) and team descriptions handed out to the groups for the group discussion</div> <div>│ Group Discussion - Handout - Processes # The printed version of the questionnaires (Coaching and Key Task Processes) and team descriptions handed out to the groups for the group discussion</div> <div>│ Individual Response - Online Survey # A PDF version of the online survey</div> <div>└───Individual Response - Results # Excel file with all the responses of the participants from the online survey. Excel sheets are organized as follows</div> <div>RAW DATA # all the raw data from the survey</div> <div>RAW DATA TRANSPOSED # raw data with rows and columns inverted</div> <div>ALL TEAMS SCORES # quantitative results about adequacy for each feature: team scores and importance</div> <div>ALL FEEDBACK PER FEATURE # qualitative results about adequacy for each feature: grouped feedback participants</div> <div>forlatex # results in latex-ready form</div> <div>forspider # results in a form that supports easy creation of spider plot</div> <div>EFFECTIVENESS # quantitative results about effectiveness measures</div> <div>FINAL COMMENTS # final qualitative feedback from participants</div> <div>essentials # RAW DATA TRANSPOSED (essentials only)</div> <div>enablers # RAW DATA TRANSPOSED (enablers only)</div> <div>processes # RAW DATA TRANSPOSED (processes only)</div> <div>*_num_scores # * in {essentials, enablers, processes}, likert-to-numerical scores</div> <div>*_num_scores_annotatio # same as *_num_scores, just with numerical values instead of formulas</div> <div>```</div> <div> </div> <div> </div> <div> </div> <div> </div> </div>
Hybrid Intelligence in Medical Image Segmentation
<p>Title of publication: Hybrid Intelligence in Medical Image Segmentation</p> <div>The original dataset contains x-rays and corresponding masks. Some masks are missing so it is advised to cross-reference the images and masks. The dataset link consisting of train and test is attached here, <br><br></div> <div><a title="Original URL: https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels/data. Click or tap if you trust this link." href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.kaggle.com%2Fdatasets%2Fnikhilpandey360%2Fchest-xray-masks-and-labels%2Fdata&data=05%7C02%7CS.Oyelere%40exeter.ac.uk%7C80fa490cf7be41cf7a6508dd0069c919%7C912a5d77fb984eeeaf321334d8f04a53%7C0%7C0%7C638667176577007815%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Fai5kYk%2FhaVfJp%2FhtFOFQfYSqcoFcpDTtiq%2FWiktUYY%3D&reserved=0" target="_blank" rel="noopener noreferrer">https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels/data</a></div> <div> </div> <div>The OP had the following request:<br>It is requested that publications resulting from the use of this data attribute the source (National Library of Medicine, National Institutes of Health, Bethesda, MD, USA and Shenzhen No.3 People’s Hospital, Guangdong Medical College, Shenzhen, China) and cite the following publications:<br>Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S, Thoma G, Wang YX, Lu PX, McDonald CJ. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014 Feb;33(2):233-45. doi: 10.1109/TMI.2013.2284099. PMID: 24108713<br>Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. PMID: 24239990</div> <div> </div> <div>The dataset is split into test and validation also so the final ones are attached as : train data, test data link and </div> <div>validation data link :<br>The clinician masks are attached as: validation mask and testing mask.</div>
Data from: Taking advantage of hybrid bioinspired intelligent algorithm with decoupled extended Kalman filter for optimizing growing and pruning radial basis function network
Open the record for dataset details and reuse information.
Hybrid Intelligence for Trustable Diagnosis and Patient Management of Prostate Cancer (HIT-PIRADS)
ClinicalTrials.gov study NCT06804785. IPD Sharing: YES. Countries: 1. Publications: 0.
ScienceDex guides
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