Label Critic: Design Data Before Models (2411.02753v1)

Abstract: As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that pre-existing AI models can largely automate this process. Following the spirit don't use a sledgehammer on a nut, we find that, rather than creating annotations from scratch, radiologists only have to review and edit errors if the Best-AI Labels have mistakes. To obtain the Best-AI Labels among multiple AI Labels, we developed an automatic tool, called Label Critic, that can assess label quality through tireless pairwise comparisons. Extensive experiments demonstrate that, when incorporated with our developed Image-Prompt pairs, pre-existing Large Vision-LLMs (LVLM), trained on natural images and texts, achieve 96.5% accuracy when choosing the best label in a pair-wise comparison, without extra fine-tuning. By transforming the manual annotation task (30-60 min/scan) into an automatic comparison task (15 sec/scan), we effectively reduce the manual efforts required from radiologists by an order of magnitude. When the Best-AI Labels are sufficiently accurate (81% depending on body structures), they will be directly adopted as the gold-standard annotations for the dataset, with lower-quality AI Labels automatically discarded. Label Critic can also check the label quality of a single AI Label with 71.8% accuracy when no alternatives are available for comparison, prompting radiologists to review and edit if the estimated quality is low (19% depending on body structures).

• The paper introduces Label Critic's automated evaluation method, achieving 96.5% accuracy in optimizing CT scan labels through pairwise comparisons.
• It employs LVLMs to rapidly detect and correct annotation errors, cutting review time from over 30 minutes to about 15 seconds per scan.
• The framework scales with minimal training samples and offers potential for extension to other imaging modalities and complex labeling tasks.

Label Critic: A Systematic Approach to Optimizing Medical Dataset Annotations

In this paper, the authors present "Label Critic," a sophisticated framework designed to streamline the annotation processes of medical datasets. As medical datasets expand, creating detailed annotations becomes increasingly cumbersome. The authors recognize the limitations of relying solely on radiologists for precise annotations and propose an AI-centric solution for error detection and correction in dataset labeling.

Overview of the Problem and Solution

Annotation of medical images, particularly CT scans, is an arduous task, traditionally executed by radiologists. Even with AI-assisted segmentation, the process is hindered by the time and effort needed to rectify labeling errors. The paper contends that AI models are capable of automating these corrections by implementing a system that can evaluate and rectify erroneous labels with minimal radiologist oversight. The proposed method, "Label Critic," is established to introduce an optimized pipeline that compares pre-existing AI-generated labels to ascertain the best quality annotation without additional training.

Key Findings and Methodology

"Label Critic" leverages Large Vision LLMs (LVLMs) such as Llava and GPT-4V, achieving a notable accuracy of 96.5% in identifying the optimal label in pair-wise comparisons. This performance is achieved without extra fine-tuning, indicating the potency of utilizing generic LVLMs trained on diverse datasets for medical image analysis. Through Image-Prompt pairs integrated into LVLMs, the system provides rust-resistant label error detection, scaling from traditional manual reviews to an automated comparison process that dramatically decreases the required review time from upwards of 30 minutes to approximately 15 seconds per scan.

The system applies an architecture that conducts 2D projections of CT scans mimicking X-ray views to capitalize on existing models’ visual comprehension abilities. Prompts tailored for distinct anatomical features improve the LVLMs’ decision-making efficacy, with the flexibility to operate with minimal training samples—allowing for the dataset-specific adoption of quality AI labels.

Implications and Future Directions

Practically, the implementation of "Label Critic" can significantly revolutionize the management of large-scale medical datasets by reducing the annotation workload on radiologists and increasing the throughput of accurate and reliable dataset generation. With its demonstrated scalability across different hospital settings without extensive datasets, the approach can standardize a measure of label quality in medical imaging benchmarks.

Theoretically, this work advances the application of vision-LLMs in a domain-specific scenario where semantic accuracy is crucial. The ability of "Label Critic" to double as a scalable tool for different anatomical structures points towards future research and enhancements in LVLM methodologies for specialized tasks.

Future work could focus on extending this framework to include detection of more complex classes such as tumors, addressing finer segmentation subtleties, and incorporating adaptive learning techniques for continuous optimization. Additionally, expanding the scope of this approach beyond CT scans to other imaging modalities may offer broader applicability in various branches of medical imaging. Integrating continuous learning mechanisms could allow for dynamic updates to the model’s interpretative frameworks as new scanning technologies and diagnostic criteria evolve.

In conclusion, "Label Critic" promises a paradigm shift in automating medical image labeling, relieving bottlenecks typical in large dataset management and enabling rapid advancements in medical diagnosis models.