·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science2h◆Scoped Semantic Invariance as a Foundation for LLM Reliability2h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation2h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems2h◆AgentOrch: How Close Can Global Orchestration Come to Step-by-Step Interaction?2h◆How To Build A Modern Diffusion Language Model2h◆Data Optimization for LLMs: A Survey2h◆LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery2h◆The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting2h◆SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents2h◆FraudSociety: A Generative Agent-Based Society for the Financial-Fraud Economy2h◆CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems2h◆Learning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing2h◆Counterfactual Model Testing Under Distribution Shift2h◆Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security2h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems2h◆Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks2h◆RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches2h◆Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models2h◆SearchJev: A Calibrated System-1 Model for Search Agents2h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science2h◆Scoped Semantic Invariance as a Foundation for LLM Reliability2h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation2h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems2h◆AgentOrch: How Close Can Global Orchestration Come to Step-by-Step Interaction?2h◆How To Build A Modern Diffusion Language Model2h◆Data Optimization for LLMs: A Survey2h◆LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery2h◆The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting2h◆SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents2h◆FraudSociety: A Generative Agent-Based Society for the Financial-Fraud Economy2h◆CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems2h◆Learning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing2h◆Counterfactual Model Testing Under Distribution Shift2h◆Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security2h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems2h◆Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks2h◆RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches2h◆Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models2h◆SearchJev: A Calibrated System-1 Model for Search Agents2h◆
News/A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging
arxiv
PublishedMay 29, 2026 at 4:00 AM
—neutral

A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.29753v1 Announce Type: cross Abstract: Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware complexity and cost. In this work, we propose a unified deep learning framework that synthesizes cont

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
01
  • 01
    unified deep learning framework
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#medical-imaging#deep-learning#image-processing#ct-scans

No replies yet. Be first.

Mentioned models
01
  • 01
    unified deep learning framework
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#medical-imaging#deep-learning#image-processing#ct-scans

Related coverage

More from ARXIV
arxivAdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science2harxivScoped Semantic Invariance as a Foundation for LLM Reliability2harxivGEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation2harxivEstimation of Propagated Risk in LLM-based Multi-Agent Systems2h
The Bubble Brief
MON · WED · FRI

Read medical-imaging insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews