Medical Technology & Art
Blending Identities and Breathing Life Into Radiology Through ML-Merged Volumetric Chimeras
This research introduces a novel workflow to create synthetic radiological chimeras by blending volumetric human forms using AI-based frame interpolation. Moving beyond traditional surface-level representations, these merged forms explore the metaphor of 'imperceptible identities,' aiming to strip away visual biases and highlight the inherent similarities of the human interior. The work also animates these previously static datasets, bringing 'life' to the blended forms and inviting contemplation on human commonalities.
Executive Impact & Key Metrics
The project challenges conventional views of the body, offering a unique artistic perspective that transcends typical visual and interpersonal biases by focusing on shared internal anatomy. It demonstrates an innovative application of machine learning in art, developing a new pipeline for volumetric data manipulation. While primarily artistic, it raises important discussions on the ethical implications of AI in medical imaging, particularly concerning demographic bias, prompting reflection on equitable healthcare and the future of identity representation.
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Blended Human Forms Created
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Bias Reduction Potential (Artistic)
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Novel Animation Techniques Implemented
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New Volumetric Blending Pipeline Developed
Deep Analysis & Enterprise Applications
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Computing methodologies
Social and professional topics
Keywords
The core innovation lies in repurposing AI-based frame interpolation (specifically FILM) to merge volumetric datasets, a technique not previously applied to this domain. The process involves meticulous data normalization (scaling, rotation, artifact removal) across axial slices, followed by alternating frames from two subjects and generating an 'inter-frame' to create a new, blended volume. For rendering, a custom stacked-transparent-image approach with additive blending and domain distortion fields enables subtle animations, simulating breathing and internal shifts. Particle systems are used to represent biological processes (blood flow, neural activity), adding dynamic visual depth.
This work deeply engages with social and professional topics related to identity, bias, and medical technology. By creating radiological chimeras that eschew surface boundaries, it directly addresses visual biases often associated with race, gender, and age, highlighting our 'inner sameness.' It draws parallels to avatars in virtual worlds, questioning how identity is constructed and perceived when physical form is abstracted. The discussion critically examines recent findings that AI radiology models can exhibit demographic bias, leading to under-diagnosis in marginalized groups, underscoring the ethical imperatives for equitable and bias-free medical AI development.
- Synthetic Radiology: Creation of artificial medical imagery that goes beyond diagnostic purposes, crafted from combined real scans.
- Machine Learning: Utilized through the FILM algorithm for frame interpolation, enabling the seamless blending of volumetric data from multiple human subjects.
- Animation: Breathing life into static radiological datasets via domain distortion fields, generative particle systems (e.g., blood flow, neural pathways), and multimodal sound design.
- Chimera: The central metaphor of the work, representing human forms that are blended from multiple individuals, transcending genetic inheritance and traditional identity boundaries.
- Avatars, Identity: Explores new forms of digital identity through interior body representations, contrasting with typical surface-biased avatars and prompting reflection on what defines the 'self' in a technologically mediated world.
Enterprise Process Flow
Import & Conform
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Export Slices (Axial)
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Remove Machinery
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Rotate & Align
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Re-slice & Crop
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Final Re-slice (Coronal)
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Alternate Frames
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FILM Inter-frame
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Final Merged Sequence
Inner Sameness
Revealed beyond surface bias through blended radiological chimeras.
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Addressing Bias in AI Radiology
While this project aims to reduce visual bias, recent studies highlight ethical challenges in AI-driven medical imaging, particularly concerning demographic bias in diagnostic models.
Challenge: AI radiology models have been shown to under-diagnose marginalized groups and exhibit racial bias, potentially exacerbating health disparities.
Solution: Continued research into bias mitigation techniques, transparent AI development, and diverse data sets for training models.
Results: Emphasizes the critical need for ethical AI development in medical technology to ensure equitable healthcare outcomes for all demographics, even as artistic explorations push boundaries.
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Annual Savings
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Hours Reclaimed
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Your AI Transformation Roadmap
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Phase 1: Data Acquisition & Normalization
Gathering diverse CT scans, developing protocols for scaling, rotation, and artifact removal to prepare data for blending, ensuring optimal input for the ML process.
Phase 2: ML-Merged Volumetric Synthesis
Implementing and refining the FILM algorithm to interpolate between axial slices of different subjects, creating novel merged volumes that maintain anatomical coherence.
Phase 3: Organ Segmentation & Artistic Refinement
Separating specific organs (e.g., heart, brain, voice box) and applying 3D modeling and texturing for artistic rendering, cleaning up topology, and optimizing for visual impact.
Phase 4: Volumetric Animation & Sound Design
Developing domain distortion fields for subtle internal movement, generative particle systems for biological processes, and multimodal soundscapes to imbue the chimeras with a sense of 'life' and presence.
Phase 5: Ethical Review & Expanded Applications
Assessing the ethical implications of synthetic radiology and exploring broader artistic and societal applications for identity and bias studies, including potential extensions to non-human forms and cross-species blending.
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