The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography
Abstract
:1. Introduction
2. Relevance and Challenges
2.1. Bring Together AI and Medical Experts
2.2. Legal, Ethics, and Data Sharing
Legal Challenges
- A retrospective study—the model training/development of the INCISIVE AI toolbox.
- A prospective observational study—the validation of the INCISIVE AI toolbox.
- A prospective feasibility study—the evaluation of the INCISIVE AI toolbox.
3. Data Preparation and Integration
3.1. Data Integration and Quality Check Tool
3.2. Annotation and De-Identification Tools
3.2.1. Annotation
- classification where the whole image is assigned a corresponding label;
- bounding box where a rectangular region of interest is assigned a corresponding label;
- segmentation where each pixel is assigned a corresponding label.
- SiteID is completed from each data provider (e.g., 001 for CERTH, 002 for ICCS, etc.).
- PatientID is created by the de-identification procedure, which took place before annotation (e.g., 000001, 000002).
- Modality is completed from each data provider (e.g., MRI, PETCT, CT, MMG, US, HP).
- Timepoint (BL, TP1, TP2, TP3).
3.2.2. De-Identification
- The real name of the patient (or the alternative identifiable information that each data provider chooses (e.g., a social security number);
- The ID (a variable number of characters for the pilot’s name (e.g., AUTH), followed by a dash, followed by a five-digit incremental number).
3.3. Interoperability Standards and Data Model
3.4. Data Collection and Integration Experiences—Breast Cancer
- a common clinical data schema, i.e., a structural embedding designed to suit all types of information;
- standards of medical terminology to be used, as agreed by a consensus among data providers;
- image de-identification (CTP DICOM anonymizer [38]);
- evaluation of the data quality and compliance (Section 3.1);
4. Results to the Existing Data Repository
4.1. Data Sharing
4.2. Federated Sharing
- The data preparation toolset is a set of tools that operate at each data provider’s site before the data federation stage so that the data becomes GDPR-compliant and appropriately processed (e.g., annotated, where relevant). The main tools that support this process are the data de-identification tool, the data annotation tool, the data curation tool, and the data quality check tool. These tools ensure that the data undergoes a correct pre-process to fulfill the system preconditions before being stored in the INCISIVE platform (see Section 3.1 data integration and 3.2 annotation and de identification).
- The federated node is the node that is hosted by the data provider where the data is stored. The data do not leave the related premises; therefore, the data partners keep full control.
- The federated space is the Cloud environment that contains the centralized services required to offer the federated INCISIVE functionalities.
4.3. INCISIVE Retrospective Data—Breast Cancer Mammograpy Collection
5. Description of AI Toolbox/Breast Cancer Tools
5.1. Federated Learning—Data Exploitation
5.1.1. How It Is Performed
- Orchestrator: in charge of receiving the training requests and deploying the required components in the central node and in the federated nodes.
- Model-as-a-service (MaaS): in charge of storing models and performing inference.
- AI federator: in charge of managing the federated learning process (send training requests, receive weights, and merge models).
- AI engine: in charge of performing the actual training at each federated node. This component is composed of auxiliary components to provide data and retrieve results from a standard machine learning application agnostic of the federated learning and the infrastructure.
- A train request is received in the UI.
- The orchestrator analyzes the request, and a specific AI federator and AI engines of the federated nodes are deployed.
- The AI federator initializes the model or loads a previously trained model from the MaaS, depending on if training from scratch or training from a pretrained model is requested.
- The AI engine receives the model and trains it, using local data available.
- The AI federator receives the trained models and merges them using a merging scheme, e.g., FedAvg [47].
- In case another federated learning round is required, the process restarts from Step 4, sending the new merged model to the AI engines.
- Finally, the model is stored in the MaaS.
5.1.2. Hybrid Infrastructure and Federated Learning
5.2. Preliminary Results—Initial Analysis on Mammography
5.2.1. Data Quality and Related Pre-Processing
5.2.2. Annotation Style
6. Conclusions and Next Steps
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Ultrasound | Mammography | MRI | CT Scan | PET/CT Scan | |
---|---|---|---|---|---|
Data requirements | Healthy and non-healthy images |
|
| MST: 5 mm | MST: 5 mm |
Annotation procedure | Bounding box | Contouring | Contouring | Bounding box | Bounding box |
Labels |
|
|
|
|
|
Partner | No of Patients | No of Studies | No of Annotated Studies | No of Images |
---|---|---|---|---|
AUTH | 25 | 85 | 71 | 36,345 |
DISBA | 11 | 26 | 19 | 14,886 |
GOC | 67 | 297 | - | 43,391 |
HCS | 1950 | 2326 | 1309 | 10,326 |
UNITOV | 11 | 615 | - | 30,331 |
UNS | 1392 | 4704 | 666 | 908,580 |
DP | MG Studies | MG Images | MG Annotated Images | DBT Studies | DBT Images | DBT Annotated Images |
---|---|---|---|---|---|---|
AUTH | 41 | 160 | 109 | 0 | 0 | 0 |
GOC | 49 | 190 | 0 | 4 | 599 | 0 |
HCS | 1950 | 7282 | 1214 | 3 | 5 | 0 |
UNS | 4119 | 15,278 | 655 | 1618 | 131,653 | 26 |
Manufacturer | Bits Allocated | Bits Stored | Image Size | Service–Object Pair Class |
---|---|---|---|---|
CARESTREAM Rochester, NY, USA | 16 | 12 | 6000 × 4735 | Computed Radiography Image Storage |
FUJIFILM corporation Midtown West, Tokyo Midtown Akasaka, Minato, Tokyo, Japan | 16 | 12, 14 | 5928 × 4728, 2370 × 1770, 4740 × 3540, 2964 × 2364 | Breast Tomosynthesis Image Storage, Computed Radiography Image Storage, Digital Mammography X-ray Image Storage—For Presentation |
HOLOGIC Inc. Marlboough, MA, USA | 16 | 10,12 | 2457 × 1892, 425 × 268, 4096 × 3328, 2457 × 1996, 3328 × 2560, 2457 × 1890 | Secondary Capture Image Storage, Breast Tomosynthesis Image Storage, Digital Mammography X-ray Image Storage—For Presentation |
IMS GIOTTO S.P.A. Sasso Marconi (BO), Italy | 16 | 14 | 2149 × 1198, 3580 × 2663, 2295 × 1120, 3580 × 2603, 1691 × 896, 3580 × 2717, 3580 × 2597, 1794 × 826, 3580 × 2812, 1817 × 876, 3580 × 2730, 3580 × 2591, 3580 × 2585, 3580 × 2657, 3580 × 2687, 1864 × 998, 3580 × 2669, 2164 × 1374, 3580 × 2675, 2287 × 1231 | Breast Tomosynthesis Image Storage, Digital Mammography X-ray Image Storage—For Presentation |
IMS S.R.L. | 16 | 14 | 3584 × 2784, 3584 × 2736, 3584 × 2720, 3584 × 2704, 3580 × 2812, 3584 × 2768, 3584 × 2752, 3584 × 2816, 3584 × 2800 | Digital Mammography X-ray Image Storage—For Presentation |
LORAD, Hologic company Bedford, MA, USA | 16 | 16 | 512 × 512, 1024 × 1024 | Digital Mammography X-ray Image Storage—For Presentation |
PHILIPS digital mammography Sweden, AB Solna, Stockholms Lan, Sweden | 16 | 16 | 5355 × 4915 | Digital Mammography X-ray Image Storage—For Presentation |
SIEMENS Munich, Germany | 16 | 12 | 1882 × 1325, 3164 × 2364, 3241 × 2203, 2577 × 1006, 3518 × 2800 | Secondary Capture Image Storage, Digital Mammography X-ray Image Storage—For Presentation |
SIEMENS Healthineers Erlangen, Germany | 16 | 12, 14 | 2850 × 2394, 3223 × 2842, 3798 × 3328, 3359 × 2776, 2786 × 2027, 4017 × 3328, 4083 × 3328, 4092 × 3328, 3986 × 3328, 2298 × 2118, 3647 × 3328, 3867 × 2195, 3842 × 3328, 4096 × 3328, 3328 × 2560, 2665 × 2394 | Secondary Capture Image Storage |
Annotation Label | |||||||
---|---|---|---|---|---|---|---|
Provider | Suspicious/Indeterminate | Malign | Calcification | Surgical Clip | Axial Lymph Node | Benign | No Findings |
AUTH | 5 | 28 | 85 | 28 | 15 | 15 | 12 |
HCS | 160 | 299 | 558 | 50 | 249 | 300 | 7 |
UNS | 213 | 53 | 87 | 86 | 87 | 52 | 216 |
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Lazic, I.; Agullo, F.; Ausso, S.; Alves, B.; Barelle, C.; Berral, J.L.; Bizopoulos, P.; Bunduc, O.; Chouvarda, I.; Dominguez, D.; et al. The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography. Appl. Sci. 2022, 12, 8755. https://doi.org/10.3390/app12178755
Lazic I, Agullo F, Ausso S, Alves B, Barelle C, Berral JL, Bizopoulos P, Bunduc O, Chouvarda I, Dominguez D, et al. The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography. Applied Sciences. 2022; 12(17):8755. https://doi.org/10.3390/app12178755
Chicago/Turabian StyleLazic, Ivan, Ferran Agullo, Susanna Ausso, Bruno Alves, Caroline Barelle, Josep Ll. Berral, Paschalis Bizopoulos, Oana Bunduc, Ioanna Chouvarda, Didier Dominguez, and et al. 2022. "The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography" Applied Sciences 12, no. 17: 8755. https://doi.org/10.3390/app12178755