add some papers

_bibliography/papers.bib

@@ -3,6 +3,58 @@
 
 @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{Jia2025,
+  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 =  {},
+  pages =   {},
+  month =   {},
+  year =    {2025},
+  pdf =     {2025_j_SR.pdf},
+  html =    {},
+  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.},
+}
+
+
+@article{Malimban2025,
+  abbr =     {},
+  bibtex_show = {true},
+  title =   {A simulation framework for preclinical proton irradiation workflow},
+  author =  {Malimban, Justin and Ludwig, Felix and Lathouwers, Danny and Staring, Marius and Verhaegen, Frank and Brandenburg, Sytze},
+  journal = {Physics in Medicine and Biology},
+  volume =  {},
+  pages =   {},
+  month =   {},
+  year =    {2025},
+  pdf =     {2025_j_PMB.pdf},
+  html =    {https://doi.org/10.1088/1361-6560/ad897f},
+  arxiv =   {},
+  code =    {},
+  abstract = {<b>Objective:</b> The integration of proton beamlines with X-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and  tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.<br><b>Approach:</b> The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.<br><b>Main Results:</b> The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90kV/Cu) and DECT (50kV/Al-90kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.<br><b>Significance:</b> The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.},
+}
+
 @article{Jia2024,
   abbr =     {},
   bibtex_show = {true},

_bibliography/papers_conf.bib

@@ -3,6 +3,25 @@
 
 @string{aps = {American Physical Society,}}
 
+@inproceedings{Chen:2024,
+  abbr =    {},
+  bibtex_show = {true},
+  author =    {Lyu, Donghang and Rao, Chinmay S. and Staring, Marius and van Osch, Matthias J.P. and Doneva, Mariya and Lamb, Hildo and Pezzotti, Nicola},
+  title =     {UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction},
+  booktitle = {Statistical Atlases and Computational Modeling of the Heart (STACOM)},
+  address =   {Marrakech, Morocco},
+  series =    {Lecture Notes in Computer Science},
+  volume =    {},
+  pages =     {},
+  month =     {October},
+  year =      {2024},
+  pdf =       {2024_c_STACOM.pdf},
+  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.},
+}
+
 @inproceedings{Chen:2024,
   abbr =    {},
   bibtex_show = {true},
@@ -11,10 +30,12 @@ @inproceedings{Chen:2024
   booktitle = {Medical Image Computing and Computer-Assisted Intervention},
   address =   {Marrakech, Morocco},
   series =    {Lecture Notes in Computer Science},
+  volume =    {15003},
+  pages =     {508 -- 518},
   month =     {October},
   year =      {2024},
   pdf =       {2024_c_MICCAI.pdf},
-  html =      {},
+  html =      {https://doi.org/10.1007/978-3-031-72384-1_48},
   arxiv =     {2404.02614},
   code =      {https://github.com/cyjdswx/DeepGrowth},
   abstract =  {Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreatment, an accurate prediction of tumor growth based on longitudinal imaging is highly desirable. In this paper, we introduce DeepGrowth, a deep learning method that incorporates neural fields and recurrent neural networks for prospective tumor growth prediction. In the proposed model, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code. Unlike previous studies, we predict the latent codes of the future tumor and generate the tumor shapes from it using a multilayer perceptron (MLP). To deal with irregular time intervals, we introduce a time-conditioned recurrent module based on a ConvLSTM and a novel temporal encoding strategy, which enables the proposed model to output varying tumor shapes over time. The experiments on an in-house longitudinal VS dataset showed that the proposed model significantly improved the performance (&ge; 1.6% Dice score and &ge; 0.20 mm 95% Hausdorff distance), in particular for top 20% tumors that grow or shrink the most (&ge; 4.6% Dice score and &ge; 0.73 mm 95% Hausdorff distance). Our code is available at <a href="https://github.com/cyjdswx/DeepGrowth">https://github.com/cyjdswx/DeepGrowth</a>.},