用深度学习提升医疗图像质量，改善诊断准确性。
Deep Perceptual Enhancement for Medical Image Analysis
- 采用全卷积网络结合残差块与门控机制，端到端优化图像感知质量。
- 在多种医学影像上，PSNR提升5.00~7.00 dB，DeltaE提升4.00~6.00。
- 显著提升后续分析任务性能，适合临床实际应用。
由于硬件限制，医疗图像采集设备常产生低质量图像（如对比度低、亮度不当、噪声大等）。这类图像直接影响诊断过程，使医生决策更加困难。本文提出一种端到端学习策略，用于增强此类低质量图像，以加速医疗图像分析任务。据我们所知，这是首个在医学影像中全面解决对比度校正、亮度校正、去噪等问题的深度方法。所提网络采用全卷积结构，结合残差块与残差门控机制，有效减少视觉伪影，并通过多目标损失函数引导生成感知上更真实的增强图像。实验验证表明，该方法在不同医学影像模态上优于现有方法，峰值信噪比（PSNR）提升5.00至7.00 dB，DeltaE提升4.00至6.00。此外，该增强方法显著提升了后续分析任务性能，展现出在真实场景中的应用潜力。代码已公开：https://github.com/sharif-apu/DPE_JBHI
原文摘要 · Abstract (English)
Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practitioners notably complicated. This study proposes to enhance such low-quality images by incorporating end-to-end learning strategies for accelerating medical image analysis tasks. To the best concern, this is the first work in medical imaging which comprehensively tackles perceptual enhancement, including contrast correction, luminance correction, denoising, etc., with a fully convolutional deep network. The proposed network leverages residual blocks and a residual gating mechanism for diminishing visual artefacts and is guided by a multi-term objective function to perceive the perceptually plausible enhanced images. The practicability of the deep medical image enhancement method has been extensively investigated with sophisticated experiments. The experimental outcomes illustrate that the proposed method could outperform the existing enhancement methods for different medical image modalities by 5.00 to 7.00 dB in peak signal-to-noise ratio (PSNR) metrics and 4.00 to 6.00 in DeltaE metrics. Additionally, the proposed method can drastically improve the medical image analysis tasks' performance and reveal the potentiality of such an enhancement method in real-world applications. Code Available: https://github.com/sharif-apu/DPE_JBHI
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