Improving Image Classification Through Explainable AI and Causality in Human-Aligned Deep Learning

On April 18, 2025, Gianluca Carloni published Human-aligned Deep Learning: Explainability, Causality, and Biological Inspiration, a comprehensive exploration of aligning deep learning with human reasoning to enhance image classification efficiency, interpretability, and robustness.

This research aligns deep learning with human reasoning for efficient image classification, focusing on explainability, causality, and biological vision. It validates an explainable-by-design method for breast mass classification, introduces a scaffold for organizing XAI and causality research, proposes modules exploiting feature co-occurrence, and develops the CROCODILE framework integrating causal concepts.

Additionally, it explores human object recognition through CoCoReco, a connectivity-inspired network. Key findings include limitations of activation maximization, effectiveness of prototypical-part methods, deep connections between XAI and causality, leveraging weak causal signals, generalizability across domains, and improved recognition via biological motifs.

Deep learning has revolutionized artificial intelligence, enabling machines to learn from data with unprecedented accuracy. Recent advancements have focused on improving model robustness, interpretability, and generalizability across diverse domains. This article explores key innovations in deep learning, drawing insights from cutting-edge research in radiomics, disentanglement methods, and domain adaptation techniques.

Radiomics, a field that extracts high-dimensional features from medical images, has seen significant progress thanks to deep learning. Researchers have developed robust frameworks to analyze nuclear medicine data, ensuring reproducibility and avoiding pitfalls such as overfitting or selection bias. These advancements are critical for translating radiomic models into clinical practice, where accuracy and reliability are paramount.

Disentanglement methods aim to separate independent factors of variation in data, enhancing model interpretability and generalization. For instance, techniques like cycle-consistent adversarial autoencoders have been employed to harmonize multi-site cortical data, effectively disentangling site-specific effects from underlying biological signals. This approach not only improves the robustness of models but also facilitates cross-domain applications.

Deep learning models often struggle with domain shifts—differences between training and test distributions. To address this, researchers have developed content-aware style-invariant models for disease detection, ensuring that models generalize well to unseen domains. These methods combine factual and counterfactual predictions to achieve fair and robust outcomes, a critical step toward deploying AI in real-world healthcare settings.

Recent work has integrated causal inference into deep learning pipelines, enabling models to move beyond mere correlation detection to understanding cause-effect relationships. Frameworks like Counterfactual Fairness combine factual and counterfactual predictions to ensure fairness in model outputs, addressing ethical concerns in AI decision-making.

Advancements in radionics, disentanglement, domain adaptation, and causal inference highlight the maturation of deep learning as a tool for solving complex real-world problems. As these techniques continue to evolve, they promise to unlock new possibilities in healthcare, computer vision, and beyond. The focus on robustness, fairness, and interpretability ensures that deep learning remains not just a powerful tool but also a responsible one.