A Diffusion-Driven Fine-Grained Nodule Synthesis Framework for Enhanced Lung Nodule Detection from Chest Radiographs

2026-03-02

AI Summary

This study addresses the challenge of limited deep learning performance in pulmonary nodule detection on chest X-rays, primarily due to the small size, high morphological variability, and scarcity of annotated data. To overcome this, the authors propose a diffusion model–based framework for fine-grained, controllable nodule synthesis. The method employs mask conditioning to guide nodule location and contour, and integrates multiple dynamically composable LoRA modules to independently modulate distinct radiological attributes. Notably, an orthogonality loss is introduced to mitigate attention overlap and non-orthogonal parameter interference during multi-feature fusion. Experiments demonstrate that the synthesized nodules significantly enhance downstream detection performance on both public and internal datasets. Radiologist evaluations confirm the clinical plausibility and fine-grained controllability of the generated nodules, with quantitative metrics outperforming existing approaches.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Early detection of lung cancer in chest radiographs (CXRs) is crucial for improving patient outcomes, yet nodule detection remains challenging due to their subtle appearance and variability in radiological characteristics like size, texture, and boundary. For robust analysis, this diversity must be well represented in training datasets for deep learning based Computer-Assisted Diagnosis (CAD) systems. However, assembling such datasets is costly and often impractical, motivating the need for realistic synthetic data generation. Existing methods lack fine-grained control over synthetic nodule generation, limiting their utility in addressing data scarcity. This paper proposes a novel diffusion-based framework with low-rank adaptation (LoRA) adapters for characteristic controlled nodule synthesis on CXRs. We begin by addressing size and shape control through nodule mask conditioned training of the base diffusion model. To achieve individual characteristic control, we train separate LoRA modules, each dedicated to a specific radiological feature. However, since nodules rarely exhibit isolated characteristics, effective multi-characteristic control requires a balanced integration of features. We address this by leveraging the dynamic composability of LoRAs and revisiting existing merging strategies. Building on this, we identify two key issues, overlapping attention regions and non-orthogonal parameter spaces. To overcome these limitations, we introduce a novel orthogonality loss term during LoRA composition training. Extensive experiments on both in-house and public datasets demonstrate improved downstream nodule detection. Radiologist evaluations confirm the fine-grained controllability of our generated nodules, and across multiple quantitative metrics, our method surpasses existing nodule generation approaches for CXRs.

Problem

• lung nodule detection
• chest radiographs
• synthetic data generation
• fine-grained control
• data scarcity

Innovation

• diffusion model
• LoRA
• fine-grained control
• lung nodule synthesis
• orthogonality loss