Vision-Language Prompt Tuning for Human-Confirmable Image Diagnosis
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Solution Overview
Problem
Existing computer-assisted visual diagnosis methods, particularly using convolutional neural networks (CNNs), require extensive labeled datasets, significant computational resources, and domain-specific expertise, limiting scalability and reproducibility, and fine-tuning general vision-language models (VLMs) is costly and inconsistent.
Innovation Solution
A human-in-the-loop active prompt tuning approach that leverages structured and unstructured information to refine vision-language models for domain-specific diagnostic tasks, using a frozen VLM with a System Prompt and iterative human review to generate a Prompt Set for consistent and accurate image classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised deep learning models (CNNs) are used for computer-assisted visual diagnosis, then diagnostic accuracy is improved, but the need for extensive labeled datasets and significant computational resources increases
Solution Approach 1:
The patent uses few-shot prompting with a small number of hand-crafted examples (5-10 images per class) instead of extensive labeled datasets. These examples are disposable in the sense that they are manually created once and then reused repeatedly for inference without requiring retraining, replacing the need for large-scale annotated data
Solution Approach 2:
The patent extracts and separates the domain-specific knowledge into hand-crafted prompt examples that are independent of the base VLM model. This extraction allows the system to use a frozen pre-trained VLM without requiring extraction of features through extensive training on domain-specific data
2Reliability
If VLMs are fine-tuned for domain-specific tasks, then task performance is improved, but training cost and time increase significantly
Solution Approach 1:
The patent performs preliminary action by hand-crafting prompt examples and system prompts before inference. These prompts are created in advance based on domain expertise and then reused for all inference tasks, eliminating the need for time-consuming fine-tuning processes for each new task
Solution Approach 2:
The patent uses copying by replicating hand-crafted prompt examples across different inference scenarios. Instead of training a new model for each task, the same prompt structure and examples are copied and adapted slightly for different domain-specific tasks, maintaining performance while avoiding retraining costs
3Ease of manufacture
If few-shot prompting is used with general VLMs, then the need for extensive training is reduced, but output consistency and reliability decrease
Solution Approach 1:
The patent introduces hand-crafted system prompts and structured prompt examples as intermediaries between the general VLM and the specific diagnostic task. These intermediary prompts guide the model's reasoning process and ensure consistent output formats, bridging the gap between general-purpose VLMs and domain-specific requirements
Solution Approach 2:
The patent changes parameters by structuring the prompt input with specific formats, temperature settings, and guidance parameters. This parameter control ensures that the VLM produces consistent, reliable outputs suitable for medical diagnostics without requiring task-specific fine-tuning
4Measurement precision
If expert review and annotation of images is performed, then diagnostic accuracy is improved, but expert time and scalability are limited
Solution Approach 1:
The patent enables self-service by allowing the VLM to perform diagnostic analysis autonomously using hand-crafted prompts. The system serves itself by generating diagnostic reports without requiring continuous expert intervention, while experts only need to review and validate results, significantly improving scalability
Data Source
AI summary
Systems and methods are provided herein for developing and deploying active-prompt-tuned, domain-specific image diagnosis and categorization applications. Processes of the present disclosure may control and provide for human-confirmable diagnostics from images, based on controlled instructions provided to vision-language models. The controlled instructions may be developed by systems provided herein, which generate system prompts and example prompt sets using active prompt tuning approaches.