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LoRCA framework enables histology to HiP-CT image translation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed LoRCA (LoRA Cycle Adaptation), a novel framework for translating histology images to Hierarchical Phase-Contrast Tomography (HiP-CT) volumes. This method utilizes a frozen DINOv3 backbone with modality-specific LoRA adapters to learn representations without requiring paired training data. LoRCA aims to improve the alignment between 2D histological sections and 3D HiP-CT volumes, outperforming existing CycleGAN methods in translation quality and structural consistency. AI

IMPACT This research could advance medical imaging analysis by improving the registration between histological and volumetric data.

RANK_REASON The cluster contains a research paper detailing a new method for image translation in a scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LoRCA framework enables histology to HiP-CT image translation

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The cluster contains a research paper detailing a new method for image translation in a scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yang Zhou, Edoardo Occhipinti, Banboye Kidzeru Elvis, Jishizhan Chen, Stathis Megas, Joseph Brunet, Joanna Purzycka, Theresa Urban, Hector Dejea, Sarah Amalia Teichmann, Menna R Clatworthy, Paul Tafforeau, Peter D Lee, Claire L Walsh ·

    LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3

    arXiv:2608.10002v1 Announce Type: cross Abstract: Hierarchical Phase-Contrast Tomography (HiP-CT) is a synchrotron based X-ray imaging technique that enables non-destructive, volumetric imaging of intact organs with multi-resolutions bridging 20 $\mu m$/voxel for whole organs to …

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