Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding (2405.19567v2)

Abstract: Vision-LLMs (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions to assist in diagnostic and treatment tasks. However, VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. This challenge is particularly pronounced in the medical domain, where we do not only require VLM outputs to be accurate in single interactions but also to be consistent with clinical reasoning and diagnostic pathways throughout multi-turn conversations. For this purpose, we propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge. These representations are utilized to (i) generate GPT-4-guided visual instruction tuning data at scale, simulating clinician-VLM conversations with demonstrations of clinical reasoning, and (ii) create an automatic reward function that evaluates the clinical validity of VLM generations throughout clinician-VLM interactions. Our algorithm eliminates the need for human involvement in training data generation or reward model construction, reducing costs compared to standard reinforcement learning with human feedback (RLHF). We apply our alignment algorithm to develop Dr-LLaVA, a conversational VLM finetuned for analyzing bone marrow pathology slides, demonstrating strong performance in multi-turn medical conversations.

Review of Dr-LLaVA: A Symbolic-reasoning Aligned Vision-LLM for Pathology Diagnostics

The paper introduces Dr-LLaVA, a vision-LLM (VLM) trained to assist in the analysis of bone marrow pathology slides through natural language interactions with clinicians. A significant challenge addressed by this research is the prevalent issue of "hallucinations" in VLMs, where the models generate text that isn’t grounded in the visual information provided. This challenge is exaggerated in the medical domain due to the scarcity of specialized multimodal datasets and the necessity for high accuracy in both single interactions and multi-turn diagnostic dialogs.

Core Contributions

The paper proposes a novel alignment algorithm based on symbolic representations of clinical reasoning, which serves dual purposes:

1. Data Generation: The symbolic rules generate large-scale visual instruction tuning data, simulating detailed clinician-VLM dialogs demonstrating clinical reasoning.
2. Automatic Reward Function: This function evaluates VLM responses for clinical validity across multi-turn dialogs.

Three main steps structure the alignment algorithm:

1. Synthesis of Clinician-VLM Conversations
   • Leveraging symbolic rules to automatically generate conversations about visual content.
2. Design of Symbolic Rewards
   • Creation of a reward function to evaluate the alignment of VLM responses with valid clinical pathways.
3. Fine-tuning for Clinical Correctness and Consistency
   • Introduction of a fine-tuning loss that rewards both the correctness of individual responses and the overall logical consistency of the conversation.

Implementation and Results

Dr-LLaVA, a conversational VLM tailored for bone marrow diagnostics, was developed using these methodologies. Its performance was assessed against state-of-the-art VLMs such as LLaVA, OpenFlamingo, and others. Dr-LLaVA demonstrated superior performance across multiple scenarios:

• Single-turn conversations: Dr-LLaVA attained a question-level accuracy ($A_Q$) of 89.6% and a conversation-level accuracy ($A_C$) of 70.0%. These metrics signify substantial improvements over other models.
• Multi-turn conversations: Evaluation involved diverse interaction styles, including standard interactions, diagnosis-first, and improvised interactions. Dr-LLaVA consistently outperformed competing models, highlighting its robustness to varied dialog structures.
• Handling Misleading Prompts: Dr-LLaVA excelled in correcting incorrect or misleading information included by clinicians in their queries, indicating a strong anchoring in visual data and symbolic clinical reasoning.

Implications and Future Directions

The research highlights significant practical and theoretical implications:

• Practical Utility: The ability to align VLMs with symbolic clinical reasoning without necessitating extensive specialist annotations represents a pivotal advancement. It reduces the cost and expertise barrier typically associated with training reliable medical conversational agents.
• Theoretical Insights: The incorporation of symbolic reasoning into VLM finetuning frameworks offers a new paradigm for addressing hallucinations. It bridges the gap between raw data-driven optimization and the integration of domain-specific knowledge.

Future research directions could explore expanding the algorithm to other medical imaging domains, enhancing the symbolic representation frameworks to cover more complex diagnostic pathways, and integrating real-time feedback from medical professionals to further refine the models. There is also potential to investigate how these symbolic reasoning frameworks adapt to evolving medical guidelines and standards.

In summary, this paper presents a methodologically rigorous approach to enhancing the reliability and practicality of VLMs in clinical settings. Dr-LLaVA's strong performance metrics set a new benchmark and pave the way for further advancements in medical AI.