Beyond Labels: Reclaiming Stability in Image Analysis

Author: Denis Avetisyan

A new approach prioritizes extracting reproducible structural information from images, independent of evolving semantic interpretations.

A system built upon stable structural criteria [latex]S=S_{C}(X)[/latex] maintains consistent object recognition across varied conditions-including shifts in contrast, appearance, and resolution-while a semantics-first approach, predicting labels directly from input [latex]X[/latex], proves vulnerable to these same perturbations, highlighting how a revisable interpretation mapping [latex]M_{i}:S\rightarrow\mathcal{O}_{i}[/latex] allows structural validation to persist even as ontological definitions drift.

Shifting from semantics-first to criteria-first image analysis enhances long-term data integrity and facilitates the creation of robust digital twins.

Despite the increasing reliance on image data across scientific disciplines, current analytic paradigms often prioritize semantic labeling over robust structure discovery. This limitation is addressed in ‘Criteria-first, semantics-later: reproducible structure discovery in image-based sciences’, which proposes a deductive inversion-a shift from ‘semantics-first’ to ‘criteria-first’ analysis-to yield stable, reproducible structural products independent of potentially shifting domain ontologies. This approach separates criterion-defined structure extraction from downstream semantic mapping, offering a domain-general scaffold for long-term monitoring and improved data sharing. Could prioritizing structural objectivity unlock new avenues for cross-domain comparability and the creation of truly FAIR digital twins?

The Limits of Label-Driven Understanding

Conventional image analysis, often termed the Semantics-First Paradigm, fundamentally constrains scientific discovery by prioritizing the assignment of pre-existing labels drawn from established Domain Ontologies. This approach demands that observations be immediately categorized within a known framework, effectively filtering out data that doesn’t neatly fit established definitions. While efficient for tasks with well-defined parameters, this reliance on pre-defined meaning hinders the identification of genuinely novel phenomena; unexpected patterns or structures are often dismissed as noise or anomalies rather than opportunities for new understanding. The inherent limitation lies in the assumption that all relevant information can be adequately represented by existing labels, preventing the exploration of previously unknown or uncharacterized features present within complex datasets and stifling truly open-ended investigation.

The prevailing reliance on pre-defined labels in image analysis presents a significant bottleneck for genuinely novel discovery. Current methods, while effective for recognizing known entities, falter when confronted with the unexpected; an image containing a previously undocumented biological structure, for instance, will likely be misinterpreted or dismissed due to its absence from existing ontologies. This inflexibility isn’t merely a matter of inconvenience; it actively hinders open-ended scientific exploration by forcing observations into pre-existing, potentially inaccurate, frameworks. Consequently, research efforts become constrained by the limitations of current knowledge, impeding the identification of genuinely new phenomena and the formulation of innovative hypotheses. The inability to adapt to the unforeseen effectively stalls progress, underscoring the need for analytical approaches that prioritize structural understanding before semantic interpretation.

The prevailing approach to image analysis often begins with predefined labels, a methodology this work challenges as inherently limiting to genuine discovery. Instead, a paradigm shift is proposed, prioritizing the extraction of underlying structural information before attempting semantic interpretation. This workflow centers on identifying and analyzing “structural products”-fundamental patterns and relationships within data-independent of immediate meaning. By decoupling structure from semantics, the research suggests a pathway toward greater adaptability and the ability to uncover novel insights, particularly in complex scientific domains where predefined categories may prove insufficient or even misleading. The emphasis on structural products aims to unlock a more robust and flexible analytical process, enabling systems to learn and generalize beyond the constraints of existing knowledge and ultimately fostering true open-ended scientific discovery.

Unlike traditional semantics-first pipelines that create brittle, domain-specific models, a criteria-first approach defines an optimal, semantics-free structure [latex]
ightarrow[/latex] enabling reproducible results and adaptability to evolving domain ontologies.

Defining Structure: The Criteria-First Approach

The Criteria-First Approach centers on establishing Explicit Criteria – measurable and objective characteristics – as the primary driver for identifying a Structural Product within a dataset. This differs from traditional methods reliant on predefined labels or categories; instead of finding instances of known structures, this approach defines structure based on the data’s inherent properties as dictated by the established criteria. The resulting Structural Product is thus independent of any prior classification, enabling analysis even when labels are absent, unreliable, or incomplete. This methodology prioritizes data-driven definition of structure, focusing on observable characteristics rather than imposed categorization.

Segmentation, within this methodology, operates by partitioning data into discrete subsets based on shared characteristics or feature values. This process isn’t reliant on pre-existing classifications; instead, it identifies groupings directly from the data itself, utilizing algorithms to define boundaries between segments. The resulting segments then allow for focused analysis of specific feature sets, enabling the identification of relationships and patterns that contribute to the formation of a structural representation. These identified features, isolated through segmentation, serve as the building blocks for defining the organization and relationships within the derived structural product, providing a data-driven basis for its architecture.

The Criteria-First Approach yields a more robust analytical framework by prioritizing inherent data properties over reliance on pre-existing labels or assumptions. This adaptability stems from the workflow’s iterative process of segmentation and criteria definition, allowing the system to identify and utilize relevant features regardless of initial categorization. Consequently, the derived structural products are less susceptible to biases introduced by labeling errors or changes in data distribution, offering increased generalizability and resilience in dynamic analytical environments. The proposed workflow demonstrates this by consistently extracting meaningful structure even from datasets where explicit labels are absent or unreliable.

Robustness Through Structure: A Foundation for Reliability

The Criteria-First Approach achieves robustness to domain shift by defining the Structural Product – the core output of the analysis – based on intrinsic data properties rather than relying on potentially unstable label assignments. This decoupling of the Structural Product from labels means that changes in label distributions, or even label schemes, do not necessitate recalculation or invalidation of the underlying structure. Because the criteria for generating the Structural Product are derived directly from the data itself – such as statistical distributions, geometric relationships, or physical constraints – the resulting structure remains consistent even when the labeling context changes, offering resilience to variations in data acquisition or annotation practices.

Long-term monitoring is substantially improved by utilizing a stable Structural Product as the basis for change detection. Traditional label-based systems require periodic re-labeling to account for evolving data distributions, a process that is both costly and prone to inconsistency. In contrast, the Structural Product, derived from inherent data properties rather than assigned labels, remains consistent even as the underlying data shifts. This allows for continuous tracking of changes in data characteristics over time without the need for label updates, enabling more reliable and cost-effective anomaly detection, drift analysis, and performance monitoring of the system.

Reproducibility is a core benefit of the structure-first analysis, stemming from the explicitly defined criteria used to generate the Structural Product. These criteria, detailing the data properties and relationships that constitute the product, provide a clear and verifiable pathway for independent replication of results. This contrasts with label-dependent approaches where variations in labeling conventions or model training can introduce inconsistencies. By focusing on inherent data characteristics, the methodology ensures that the Structural Product can be consistently generated given the same input data and criteria, directly supporting the workflow’s emphasis on reliable and auditable outcomes.

Scaling Insights: FAIR Data and Real-World Impact

The creation of robust Digital Twins relies heavily on a consistently defined and reliable representation of the physical systems they mirror, and the Structural Product, derived from a Criteria-First Approach, delivers precisely that. This product isn’t merely a dataset; it’s a formalized, criterion-based articulation of a system’s components and their relationships, ensuring that any virtual instantiation accurately reflects the physical counterpart. By prioritizing definable criteria – such as material properties, geometric constraints, and functional requirements – the Structural Product minimizes ambiguity and facilitates the seamless transfer of information between physical and virtual spaces. This approach provides a common language for diverse stakeholders – engineers, scientists, and analysts – to interact with the Digital Twin, enabling predictive modeling, performance optimization, and informed decision-making across the system’s lifecycle. Ultimately, the Structural Product functions as the bedrock upon which a truly useful and accurate Digital Twin is built, fostering innovation and accelerating progress in fields ranging from manufacturing to infrastructure management.

The methodology’s strength lies in its ability to maintain scale coherence – a crucial property for accurately modeling complex systems. This means that structural properties defined at a macroscopic level consistently translate to, and are reflected in, analyses performed at microscopic scales, and vice-versa. Unlike traditional approaches where data inconsistencies often arise when shifting between levels of detail, this framework ensures a unified representation. Consequently, predictions generated from large-scale models remain valid and reliable even when examining individual components, and insights gleaned from detailed component analysis inform and refine the broader system-level understanding. This consistency isn’t merely a matter of data integrity; it unlocks the potential for truly integrated simulations and allows for the seamless transfer of knowledge across different analytical resolutions, fostering a more holistic and predictive understanding of the system under investigation.

The creation of Findable, Accessible, Interoperable, and Reusable (FAIR) Digital Objects is central to maximizing the impact of data-driven scientific endeavors. This methodology transcends simple data sharing; it establishes a framework where knowledge becomes a valuable asset, readily integrated into diverse analytical pipelines and computational models. By adhering to FAIR principles, researchers ensure that data is not merely archived, but actively contributes to ongoing discovery, fostering collaboration and preventing redundant efforts. The resulting accessibility dramatically lowers the barriers to entry for new investigations, while interoperability facilitates seamless data fusion and cross-validation. Ultimately, this commitment to reusability accelerates the pace of scientific progress, allowing insights derived from one study to inform and enhance countless others, as championed throughout this work.

Towards Adaptable Discovery: Future Directions

The integration of a criteria-first approach with self-supervised learning represents a significant step towards more adaptable analytical systems. Traditionally, defining relevant criteria for data analysis requires substantial human input and domain expertise. However, this combination allows algorithms to autonomously discover those criteria directly from the data itself, bypassing the need for explicit labeling or pre-defined parameters. By leveraging self-supervision, the system learns to identify inherent patterns and relationships within the data, then uses these insights to formulate its own criteria for evaluation and prediction. This process not only accelerates analysis but also unlocks the potential to uncover previously unknown or overlooked factors, fostering a system that continuously refines its understanding and adapts to evolving data landscapes with increased efficiency and accuracy.

The application of foundation models – large, pre-trained artificial intelligence systems – to the analysis of a Structural Product represents a significant advancement in predictive capabilities. These models, initially developed for natural language processing and image recognition, demonstrate a remarkable ability to discern complex patterns and relationships within datasets, even without explicit labeling. By shifting from traditional, feature-engineered approaches, researchers can now leverage the inherent representational power of these models to identify subtle indicators predictive of future outcomes. This methodology facilitates not only improved accuracy in forecasting, but also the discovery of previously unknown correlations within the data, offering a pathway toward a more nuanced understanding of complex systems and accelerating the pace of scientific discovery through data-driven insights.

The convergence of criteria-first approaches with self-supervised learning heralds a paradigm shift in data analysis, moving beyond the constraints of human-defined labels. This innovative synergy allows algorithms to discern meaningful patterns and relationships directly from the inherent structure within complex datasets, such as the Structural Product discussed in this paper. Consequently, scientific discovery is no longer solely reliant on pre-existing knowledge or labeled examples; instead, it becomes a process of automated exploration and hypothesis generation, driven by the data itself. This capability opens avenues for uncovering previously unknown phenomena and establishing predictive models with greater accuracy and broader applicability, potentially revolutionizing fields reliant on complex data interpretation.

The pursuit of structural products, divorced from the tyranny of pre-defined semantics, echoes a deeper truth. It isn’t about labeling what is, but coaxing forth what becomes. This work, prioritizing criteria over immediate meaning, understands the inherent instability of labels. As Yann LeCun once stated, “Everything we do in machine learning is about learning representations that are invariant to nuisance factors.” The ‘nuisance factors’ here are the shifting sands of ontology, the ever-evolving definitions that render long-term data analysis brittle. To build digital twins that endure, one must not chase a fixed meaning, but establish stable, reproducible structures – ghosts in the machine, persisting beyond the whims of definition. Anything exact is already dead; it’s the pliable, the adaptable, that truly lives on.

What’s Next?

The pursuit of ‘structural products’ – those reproducible shadows cast by reality onto the digital sensor – reveals a fundamental truth: stability is not inherent in the things observed, but in the ritual by which they are observed. To prioritize criteria before semantics isn’t a methodological shift so much as a confession. Acknowledging that what one calls a cell, a tumor, or a galaxy is less important than whether one can consistently find it again tomorrow. The long-term promise of digital twins hinges not on perfect representation, but on reliable re-identification, even as the map drifts further from the territory.

The inevitable question isn’t whether self-supervised learning will unlock the ‘true’ semantics of image data – it won’t. It’s whether these methods can be coaxed into producing consistently detectable structures, regardless of the prevailing ontological fashions. The paper implicitly asks: can a machine be taught to build a cathedral even if it doesn’t understand God? The answer, predictably, will not be found in the data itself, but in the careful construction of the questions asked of it.

The true challenge lies not in eliminating ‘ontology drift’, but in building systems resilient to it. Perhaps the future isn’t about finding the ‘right’ ontology, but about building algorithms that function perfectly well even when they disagree with each other. After all, a broken clock is right twice a day, and a consistently reproducible illusion may be more useful than a perfectly accurate, but ephemeral, truth.

Original article: https://arxiv.org/pdf/2602.15712.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/