try2

Author: mstaring

_bibliography/papers.bib

@@ -3,39 +3,6 @@
 
 @string{aps = {American Physical Society,}}
 
-@article{ChavezDePlaza2025,
-  abbr =     {},
-  bibtex_show = {true},
-  title =   {Implementation of Delineation Error Detection Systems in Time-Critical Radiotherapy: Do AI-Supported Optimization and Human Preferences Meet?},
-  author =  {Chaves-de-Plaza, Nicolas F. and Mody, Prerak and Hildebrandt, Klaus  and Staring, Marius and Astreinidou, Eleftheria and de Ridder, Mischa and de Ridder, Huib and Vilanova, Anna and van Egmond, Rene},
-  journal = {Cognition, Technology & Work},
-  volume =  {},
-  pages =   {},
-  month =   {},
-  year =    {2025},
-  pdf =     {2025_j_CTW.pdf},
-  html =    {},
-  arxiv =   {},
-  code =    {},
-  abstract = {},
-}
-
-@article{Jia2024,
-  abbr =     {},
-  bibtex_show = {true},
-  title =   {Explainable fully automated CT scoring of interstitiallung disease for patients suspected of systemicsclerosis by cascaded regression neural networks and its comparison with experts},
-  author =  {Jia, Jingnan and Hern{\'a}ndez Gir{\'o}n, Irene and Schouffoer, Anne A. and De Vries-Bouwstra, Jeska K. and Ninaber, Maarten K. and Korving, Julie C. and Staring, Marius and Kroft, Lucia J.M. and Stoel, Berend C.},
-  journal = {Scientific Reports},
-  volume =  {14},
-  pages =   {26666},
-  month =   {},
-  year =    {2024},
-  pdf =     {2024_j_SR.pdf},
-  html =    {https://doi.org/10.1038/s41598-024-78393-4},
-  arxiv =   {},
-  code =    {},
-  abstract = {Visual scoring of interstitial lung disease in systemic sclerosis (SSc-ILD) from CT scans is laborious, subjective and time-consuming. This study aims to develop a deep learning framework to automate SSc-ILD scoring. The automated framework is a cascade of two neural networks. The first network selects the craniocaudal positions of the five scoring levels. Subsequently, for each level, the second network estimates the ratio of three patterns to the total lung area: the total extent of disease (TOT), ground glass (GG) and reticulation (RET). To overcome the score imbalance in the second network, we propose a method to augment the training dataset with synthetic data. To explain the network's output, a heat map method is introduced to highlight the candidate interstitial lung disease regions. The explainability of heat maps was evaluated by two human experts and a quantitative method that uses the heat map to produce the score. The results show that our framework achieved a κ of 0.66, 0.58, and 0.65, for the TOT, GG and RET scoring, respectively. Both experts agreed with the heat maps in 91%, 90% and 80% of cases, respectively. Therefore, it is feasible to develop a framework for automated SSc-ILD scoring, which performs competitively with human experts and provides high-quality explanations using heat maps. Confirming the model’s generalizability is needed in future studies.},
-}
 
 
 @article{Malimban2025,

_bibliography/papers_conf.bib

@@ -19,7 +19,7 @@ @inproceedings{Chen:2024
   html =      {},
   arxiv =     {},
   code =      {},
-  abstract =  {Cardiac magnetic resonance imaging (CMR) is a crucial tool for diagnosing and treating cardiac diseases. However, the lengthy scanning time remains a significant drawback. To address this, accelerated imaging techniques have been introduced by undersampling k-space, which reduces the quality of the resulting images. Recent advancements in deep learning have aimed to expedite the scanning process while maintaining the high image quality. However, deep learning models still struggle to adapt to different sampling modes, and achieving generalization across a wide range of undersampling factors remains challenging. Therefore, an effective universal model for processing random undersampling is essential and promising. In this work, we introduce UPCMR, an unrolled model designed for random sampling CMR reconstruction. This model incorporates two kinds of learnable prompts, undersamplingspecific prompt and spatial-specific prompt, and combines them with the UNet structure in each block, aiming to provide an effective and versatile solution for the above challenge.},
+  abstract =  {Cardiac magnetic resonance imaging (CMR) is a crucial tool for diagnosing and treating cardiac diseases. However, the lengthy scanning time remains a significant drawback. To address this, accelerated imaging techniques have been introduced by undersampling k-space, which reduces the quality of the resulting images. Recent advancements in deep learning have aimed to expedite the scanning process while maintaining the high image quality. However, deep learning models still struggle to adapt to different sampling modes, and achieving generalization across a wide range of undersampling factors remains challenging. Therefore, an effective universal model for processing random undersampling is essential and promising. In this work, we introduce UPCMR, an unrolled model designed for random sampling CMR reconstruction. This model incorporates two kinds of learnable prompts, undersampling-specific prompt and spatial-specific prompt, and combines them with the UNet structure in each block, aiming to provide an effective and versatile solution for the above challenge.},
 }
 
 @inproceedings{Chen:2024,