RadAI-ImplantDetect is a pioneering project aimed at leveraging artificial intelligence to enhance the analysis of radiographic images, specifically focusing on the detection of surgical implants in hip radiographs. Utilizing a blend of semi-supervised learning, deep learning, and image preprocessing techniques, this project seeks to normalize image data for consistency and employ advanced algorithms to accurately identify and classify surgical implants.
Figure 1: Random 400 Samples X-rays
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| No surgical implants | It has surgical implants |
Figure 2: Comparison of hip X-rays
- Anomaly Detection: Implement a robust computer vision system to distinguish between natural and implanted hip radiographs.
- Image Standardization: Develop machine learning scripts to normalize the brightness and contrast across the dataset, ensuring uniformity.
- Framework Adaptability: Create an adaptable and scalable framework that can be extended to analyze radiographs of different body parts.
Our methodology incorporates a semi-supervised learning approach to efficiently use a limited number of manually labeled images as a basis for training models on a larger, unlabeled dataset. We plan to utilize Convolutional Neural Networks (CNNs) for pattern recognition within the images, alongside transfer learning to enhance model generalization across various radiographic datasets. Preprocessing techniques will standardize the image quality across the dataset, and exploratory use of unsupervised learning and clustering algorithms will refine our anomaly detection capabilities.
RadAI-ImplantDetect is an open-source project developed as part of a broader academic research initiative in collaboration with ASAxLab (https://asaxlab.github.io/). While we embrace the principles of open-source development and aim to make our findings and tools widely accessible, the project is currently closed to external contributions. This policy is in place due to the project's nature as a school project and its integration within a larger study framework.
- Open-Source Nature: The project's codebase and documentation are available for public use, study, and adaptation, in line with our commitment to open science and knowledge sharing.
- Closed to External Contributions: At this stage, we are not accepting contributions from individuals outside the project team or ASAxLab. This approach allows us to maintain a focused and coordinated effort as we navigate the complexities of this specialized field of research.
- Future Collaboration: We are keen on expanding our collaboration network in the future. Interested researchers and professionals can stay updated on our progress through our website and reach out via [insert email address] for potential collaboration opportunities beyond the project's current scope.
As of the latest update, RadAI-ImplantDetect has made significant strides in dataset preparation and normalization - a critical step toward the implementation of our semi-supervised learning model. Key achievements include:
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Manual Labeling: We have completed the manual labeling of our entire dataset, which now includes clearly identified radiographs with and without surgical implants. This manual effort has established a ground truth for further training and validation of our models.
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Image Normalization: We have successfully applied image normalization techniques to our dataset, ensuring uniform brightness and contrast across all images. This preprocessing step enhances the consistency of the data fed into our algorithms, thereby improving the learning process.
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8-bit Rendering: The original 16-bit DICOM images have been rendered into 8-bit PNG format, balancing the need for detailed radiographic information with computational efficiency. A normalization process tailored for our specific image characteristics was implemented to avoid loss of detail, with a focus on maintaining the integrity of radiographic features critical for implant detection.
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Preprocessing Script Enhancement: Our preprocessing scripts have been fine-tuned to handle the nuances of medical image processing, including the adjustment of gamma values and the careful consideration of image dynamic ranges during the bit-depth conversion.
With these crucial steps completed, we are poised to move forward into the next phase of development, which involves training our semi-supervised learning models and iteratively refining our algorithms through a series of tests and validations.
We are committed to keeping the community informed on our progress and will provide updates as our project continues to evolve. Stay tuned for upcoming milestones as we work toward realizing the full potential of AI in radiographic analysis.
RadAI-ImplantDetect is licensed under the GNU General Public License v3.0. This license allows for the modification, distribution, and use of this software freely, but it requires that any modifications and derived works also be open-source under the same license. This ensures that the project and any advancements made from it remain accessible and beneficial to the community.
For more details, see the LICENSE file included in this repository.