DPC-QA Net: A No-Reference Dual-Stream Perceptual and Cellular Quality Assessment Network for Histopathology Images

• URL: http://arxiv.org/abs/2509.15802v1
• Date: Fri, 19 Sep 2025 09:30:13 GMT
• Title: DPC-QA Net: A No-Reference Dual-Stream Perceptual and Cellular Quality Assessment Network for Histopathology Images
• Abstract summary: We present DPC-QA Net, a no-reference dual-stream network that couples wavelet-based global difference perception with cellular quality assessment.<n>Across different datasets, our model detects staining, membrane, and nuclear issues with >92% accuracy and aligns well with usability scores.
• Score: 10.861223883436885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable whole slide imaging (WSI) hinges on image quality,yet staining artefacts, defocus, and cellular degradations are common. We present DPC-QA Net, a no-reference dual-stream network that couples wavelet-based global difference perception with cellular quality assessment from nuclear and membrane embeddings via an Aggr-RWKV module. Cross-attention fusion and multi-term losses align perceptual and cellular cues. Across different datasets, our model detects staining, membrane, and nuclear issues with >92% accuracy and aligns well with usability scores; on LIVEC and KonIQ it outperforms state-of-the-art NR-IQA. A downstream study further shows strong positive correlations between predicted quality and cell recognition accuracy (e.g., nuclei PQ/Dice, membrane boundary F-score), enabling practical pre-screening of WSI regions for computational pathology.

Related papers

• ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment [20.78704478280648]
  In abdominal CT imaging, developing a low-dose, no-reference image quality assessment model mimics doctors' reading habits for evaluating CT image quality.<n>This paper proposes a novel deep learning-based framework, ClinReadNet, whose design aligns with the clinical reading logic of radiologists.<n> Experiments conducted on the LDCTIQAG2023 dataset show that the proposed method achieves the current state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2026-06-09T03:34:11Z)
• Cross-channel Perception Learning for H&E-to-IHC Virtual Staining [16.604685889132995]
  Cross-Channel Perception Learning (CCPL) strategy decomposes HER2chemical staining into Hematoxylin and DAB staining channels.<n> CCPL extracts dual-channel features from both the generated and real images and measures cross-channel correlations between nuclei and membranes.<n>Results: CCPL effectively preserves pathological features, generates high-quality virtual stained images, and provides robust support for automated pathological diagnosis using multimedia medical data.
  arXiv  Detail & Related papers  (2025-06-09T08:54:15Z)
• Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger [76.36315347198195]
  No-Reference Image Quality Assessment (NR-IQA) plays a critical role in evaluating and optimizing computer vision systems.<n>Recent research indicates that NR-IQA models are susceptible to adversarial attacks.<n>We present a novel poisoning-based backdoor attack against NR-IQA (BAIQA)
  arXiv  Detail & Related papers  (2024-12-10T08:07:19Z)
• DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild [73.6767681305851]
  Blind image quality assessment (IQA) in the wild presents significant challenges.<n>Given the difficulty in collecting large-scale training data, leveraging limited data to develop a model with strong generalization remains an open problem.<n>Motivated by the robust image perception capabilities of pre-trained text-to-image (T2I) diffusion models, we propose a novel IQA method, diffusion priors-based IQA.
  arXiv  Detail & Related papers  (2024-05-30T12:32:35Z)
• Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment [2.290956583394892]
  We propose to build a trustworthy IQA model via Causal Perception inspired Representation Learning (CPRL) CPRL serves as the causation of the subjective quality label, which is invariant to the imperceptible adversarial perturbations. Experiments on four benchmark databases show that the proposed CPRL method outperforms many state-of-the-art adversarial defense methods.
  arXiv  Detail & Related papers  (2024-04-30T13:55:30Z)
• Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives [15.575900555433863]
  We introduce a new framework of correlation-error-based attacks that perturb both the correlation within an image set and score changes on individual images. Our research focuses on ranking-related correlation metrics like Spearman's Rank-Order Correlation Coefficient (SROCC) and prediction error-related metrics like Mean Squared Error (MSE)
  arXiv  Detail & Related papers  (2024-04-20T05:24:06Z)
• Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment [49.36799270585947]
  No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference. We propose a novel contrastive pre-training framework tailored for PCQA (CoPA) Our method outperforms the state-of-the-art PCQA methods on popular benchmarks.
  arXiv  Detail & Related papers  (2024-03-15T07:16:07Z)
• When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents [92.45867913876691]
  No-reference image quality assessment (NR-IQA) models can effectively quantify perceived image quality.<n>We show that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement.
  arXiv  Detail & Related papers  (2024-03-11T03:35:41Z)
• Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity [55.399230250413986]
  We propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) to remove harmful semantic noise features from the upstream task.<n>Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
  arXiv  Detail & Related papers  (2023-12-11T06:50:27Z)
• You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment [45.62136459502005]
  We propose a network to perform full reference (FR) and no reference (NR) IQA. We first employ an encoder to extract multi-level features from input images. A Hierarchical Attention (HA) module is proposed as a universal adapter for both FR and NR inputs. A Semantic Distortion Aware (SDA) module is proposed to examine feature correlations between shallow and deep layers of the encoder.
  arXiv  Detail & Related papers  (2023-10-14T11:03:04Z)
• Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification [0.1420200946324199]
  This paper focuses on image quality assessment (IQA) using Convolutional Neural Networks (CNNs) The system's building blocks are tailored to seamlessly integrate IQA, ensuring reliable performance in disease classification.
  arXiv  Detail & Related papers  (2023-08-11T10:09:08Z)
• DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space [67.07476542850566]
  We propose to model the quality degradation in perceptual space from a statistical distribution perspective. The quality is measured based upon the Wasserstein distance in the deep feature domain. The deep Wasserstein distance (DeepWSD) performed on features from neural networks enjoys better interpretability of the quality contamination.
  arXiv  Detail & Related papers  (2022-08-05T02:46:12Z)
• CONVIQT: Contrastive Video Quality Estimator [63.749184706461826]
  Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.
  arXiv  Detail & Related papers  (2022-06-29T15:22:01Z)
• Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network [20.835800149919145]
  Image quality assessment (IQA) algorithm aims to quantify the human perception of image quality. There is a performance drop when assessing distortion images generated by generative adversarial network (GAN) with seemingly realistic texture. We propose an Attention-based Hybrid Image Quality Assessment Network (AHIQ) to deal with the challenge and get better performance on the GAN-based IQA task.
  arXiv  Detail & Related papers  (2022-04-22T03:59:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.