Advances in Document Image Rectification and Digital Twins in Healthcare

The field of document image rectification and digital twins in healthcare is experiencing significant growth, driven by advancements in machine learning and data-driven modeling. Researchers are exploring innovative approaches to improve document image rectification, including self-adaptive multitask fusion and joint low-level and high-level textual representation learning. In the healthcare domain, digital twins are being developed to enhance resource planning, patient flow, and personalized decision-making in oncology. Notable papers in this area include Document Image Rectification Bases on Self-Adaptive Multitask Fusion, which proposes a novel rectification network, and Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology, which develops an end-to-end data-to-decisions methodology for personalized medicine. Additionally, papers like BedreFlyt and CEC-Zero demonstrate the potential of digital twins and large language models in improving patient care and Chinese text processing capabilities.

Sources

Document Image Rectification Bases on Self-Adaptive Multitask Fusion

BedreFlyt: Improving Patient Flows through Hospital Wards with Digital Twins

Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies

TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data Augmentation

Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology

CEC-Zero: Chinese Error Correction Solution Based on LLM

Promise of Data-Driven Modeling and Decision Support for Precision Oncology and Theranostics

A Comparative Study of SMT and MILP for the Nurse Rostering Problem

Built with on top of