{"ID":2868738,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15802","arxiv_id":"2509.15802","title":"DPC-QA Net: A No-Reference Dual-Stream Perceptual and Cellular Quality Assessment Network for Histopathology Images","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 \u003e92% 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.","short_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...","url_abs":"https://arxiv.org/abs/2509.15802","url_pdf":"https://arxiv.org/pdf/2509.15802v1","authors":"[\"Qijun Yang\",\"Boyang Wang\",\"Hujun Yin\"]","published":"2025-09-19T09:30:13Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}