Deep Perceptual Enhancement for Medical Image Analysis

2022-04-19

IEEE journal of biomedical and health informatics

AI Summary

To address perceptual degradations—including low contrast, brightness imbalance, and noise—caused by hardware limitations in medical imaging devices, this work proposes an end-to-end trainable fully convolutional deep network, the first to systematically unify multi-dimensional perceptual enhancement tasks in medical imaging. The method introduces a residual gating mechanism to suppress visual artifacts during enhancement and employs a multi-objective perceptual loss function jointly optimizing PSNR, LPIPS, and DeltaE. Evaluated across multiple medical imaging modalities, the approach achieves PSNR gains of 5.00–7.00 dB and DeltaE reductions of 4.00–6.00, while significantly improving downstream lesion segmentation and classification performance. These results demonstrate both clinical applicability and strong generalization capability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Enhance low-quality medical images for better diagnosis
• Improve medical image analysis with deep learning
• Reduce visual artefacts in medical imaging

Innovation

• End-to-end learning for medical image enhancement
• Fully convolutional deep network with residual blocks
• Multi-term objective function for perceptual quality