StainPresetNet is a new framework for stain normalization that combines structural preservation with dataset-level color mapping while remaining computationally efficient. It uses pixel-wise normalization guided by preset reference images, allowing multi-directional adaptability without retraining. Experiments on cytopathology and histopathology datasets show that it outperforms conventional methods in color mapping accuracy, improves classifier generalization, and cuts computational overhead by 90% compared to existing deep learning approaches.
By Hongtao Kang, Die Luo, Li Chen, Jing Cai, Junbo Hu, Xiuli Liu, Shenghua Cheng
arXiv:2609.14495v1 Announce Type: new
Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently...
By Yunkai Zhuang, Qihang Yan, Zicheng Zhang, Guangtao Zhai
Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence.
arXiv:2608.21847v1 Announce Type: new
Abstract: Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport c...
By Yi Ai, Zheng Chen, Yuanhao Cai, Yulun Zhang, Xiaokang Yang
InstEditSeg is a generative framework that treats medical segmentation as an instruction-driven image editing task. Instead of producing binary masks, it renders a color-coded overlay on the original image guided by textual instructions, leveraging latent diffusion models to align with natural image distributions and reduce domain gaps. The method incorporates a DINOv3 visual encoder and a multi-scale feature pyramid fused into the diffusion U‑Net, and uses a dual‑branch classifier‑free guidance strategy to lower inference cost, achieving competitive accuracy on polyp and skin lesion datasets while improving cross‑domain generalization and multi‑lesion segmentation.
By Ziquan Liu, Zhewei Zhu, Xuyang Shi
Paint-Anything introduces a unified hex-prompt interface that allows users to specify any 24‑bit hex color for both image generation and editing. The method trains on a new Paint‑500K dataset created from real images with object grounding, perceptual color labeling, and editing‑pair synthesis, and supplements this with pure‑color anchors to address shadow‑induced color inaccuracies. Evaluated on the newly proposed Any Color Benchmark (ACBench), Paint‑Anything achieves significant improvements over the base FLUX.2‑4B model, boosting T2I and editing scores by 85.3 % and 28.3 % respectively, and outperforms competing methods on the CompColor metric.
By Ji Xie, Dewei Zhou, Xinyu Huang, Zhennan Chen, Xun Wang
arXiv:2609.36400v1 Announce Type: cross
Abstract: Deep learning classifiers for dermoscopic skin lesions often reach high in-distribution accuracy while quietly relying on spurious background cues su...
By Youssef Attia, Debasmita Mukherjee
arXiv:2606.13625v2 Announce Type: replace
Abstract: Vehicle color recognition is an important cue for vehicle identification in surveillance systems, especially when license plates are illegible due...
By Vin\'icius Orr\'u, Bruno H. Foggiatto, Gabriel E. Lima, David Menotti, Rayson Laroca
arXiv:2608.28923v1 Announce Type: cross
Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...
By Noah Videcrantz, Mostafa Mehdipour Ghazi
The paper introduces FOCUS, a cross‑dataset benchmark for retinal fundus models that evaluates vision‑only encoders, vision‑language dual encoders, and multimodal large language models across ten public datasets. It measures ranking performance, calibration, subgroup disparities, and image‑quality robustness for tasks such as diabetic retinopathy and glaucomatous optic neuropathy. Results show no single model family dominates, with vision‑only encoders ranking highest on average, while fine‑tuning improves in‑domain performance but varies in external transfer.
By David Restrepo, Chenwei Wu, Luis Filipe Nakayama, Miguel L. Martins, Stergios Christodoulidis, Maria Vakalopoulou, Enzo Ferrante
PhaseAT introduces a Fourier phase‑aware adversarial training framework to improve medical image domain generalization. By perturbing only the phase spectrum while keeping amplitude unchanged, the method stresses spatial organization under consistent appearance statistics, applying updates to the luminance channel and using a phase‑saliency mask to focus on influential frequencies. Experiments on two challenging medical datasets show PhaseAT achieves over 20% improvement in single‑source domain generalization compared to several state‑of‑the‑art methods.
By Ahmed Sharshar, Asif Hanif, Naveen Kumar Kummari, Mohammad Yaqub, Mohsen Guizan
arXiv:2603. 18846v3 Announce Type: replace-cross Abstract: Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL).
By Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi, Philipp Berens