Foundation Models and Self-Supervised Learning in Computational Histopathology and Dermatology

Current Trends in Computational Histopathology and Dermatology

Recent advancements in computational histopathology and dermatology have been driven by the development and application of foundation models, which aim to address the challenges of data scarcity and batch effects. These models, pretrained on vast datasets, are being increasingly utilized to enhance the generalization and robustness of AI systems in medical diagnostics. Notably, the integration of self-supervised learning techniques has shown promise in creating scalable models that perform well across diverse clinical settings, even in resource-limited environments. Additionally, the introduction of concept-based approaches and contrastive language prompting methods is contributing to improved interpretability and reduced false positives in diagnostic systems, thereby increasing trust and reliability in AI-driven medical decisions.

In the realm of anomaly detection, zero-shot learning methods, particularly those leveraging vision-language models like CLIP, are being explored for their potential to identify anomalies without extensive task-specific training. However, these models still require further adaptation to meet the precision demands of clinical applications. Overall, the field is moving towards more interpretable, scalable, and robust AI systems that can effectively support clinical decision-making.

Noteworthy Developments

  • Histopathological Foundation Models: Demonstrate the persistence of batch effects, necessitating more robust pretraining strategies.
  • Scalable Foundation Models for Dermatology: Show superior performance in diagnostic tasks, emphasizing the importance of domain-specific pretraining.
  • Two-Step Concept-Based Approach: Enhances interpretability and trust in skin lesion diagnosis without increased annotation burden.
  • GlocalCLIP: Innovates in zero-shot anomaly detection by optimizing global and local prompts for better anomaly pattern recognition.

Sources

Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study

Towards Scalable Foundation Models for Digital Dermatology

A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion Diagnosis

Concept Bottleneck Language Models For protein design

GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection

Mitigating covariate shift in non-colocated data with learned parameter priors

Increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches

Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection

Automatic dataset shift identification to support root cause analysis of AI performance drift

Classification and Morphological Analysis of DLBCL Subtypes in H\&E-Stained Slides

Classification of Keratitis from Eye Corneal Photographs using Deep Learning

Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?

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