Abstract is missing.
- Multi-center Ovarian Tumor Classification Using Hierarchical Transformer-Based Multiple-Instance LearningCris H. B. Claessens, Eloy W. R. Schultz, Anna Koch, Ingrid Nies, Terese A. E. Hellström, Joost Nederend, Ilse Niers-Stobbe, Annemarie Bruining, Jurgen M. J. Piek, Peter H. N. de With, Fons van der Sommen. 3-13 [doi]
- FoTNet Enables Preoperative Differentiation of Malignant Brain Tumors with Deep LearningChenyi Hong, Hualiang Wang, Zhuoxuan Wu, Zuozhu Liu, Junhui Lv. 14-25 [doi]
- Classification of Endoscopy and Video Capsule Images Using CNN-Transformer ModelAliza Subedi, Smriti Regmi, Nisha Regmi, Bhumi Bhusal, Ulas Bagci, Debesh Jha. 26-36 [doi]
- Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain MetastasesTobias R. Bodenmann, Nelson Gil, Felix J. Dorfner, Mason C. Cleveland, Jay B. Patel, Shreyas Bhat Brahmavar, Melisa S. Guelen, Dagoberto Pulido-Arias, Jayashree Kalpathy-Cramer, Jean-Philippe Thiran, Bruce R. Rosen, Elizabeth R. Gerstner, Albert E. Kim, Christopher P. Bridge. 37-47 [doi]
- Seeing More with Less: Meta-learning and Diffusion Models for Tumor Characterization in Low-Data SettingsEva Pachetti, Sara Colantonio. 48-58 [doi]
- Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer ClassificationSadam Hussain, Mansoor Ali Teevno, Usman Naseem, Beatriz Alejandra Bosques Palomo, Mario Alexis Monsivais Molina, Jorge Alberto Garza Abdala, Daly Betzabeth Avendano Avalos, Servando Cardona-Huerta, T. Aaron Gulliver, José Gerardo Tamez-Peña. 59-69 [doi]
- On Undesired Emergent Behaviors in Compound Prostate Cancer Detection SystemsErlend Sortland Rolfsnes, Philip Thangngat, Trygve Eftestøl, Tobias Nordström, Fredrik Jäderling, Martin Eklund, Alvaro Fernandez-Quilez. 73-82 [doi]
- Optimizing Multi-expert Consensus for Classification and Precise Localization of Barrett's NeoplasiaCarolus H. J. Kusters, T. G. W. Boers, Tim J. M. Jaspers, Martijn R. Jong, Rixta A. H. van Eijck van Heslinga, Albert J. de Groof, Jacques J. Bergman, Fons van der Sommen, Peter H. N. de With. 83-92 [doi]
- Automated Hepatocellular Carcinoma Analysis in Multi-phase CT with Deep LearningKrzysztof Kotowski, Bartosz Machura, Damian Kucharski, Benjamín Gutiérrez-Becker, Agata Krason, Jean Tessier, Jakub Nalepa. 93-103 [doi]
- Refining Deep Learning Segmentation Maps with a Local Thresholding Approach: Application to Liver Surface Nodularity Quantification in CTSisi Yang, Alexandre Bône, Thomas Decaens, Joan Alexis Glaunès. 104-113 [doi]
- Uncertainty-Aware Deep Learning Classification for MRI-Based Prostate Cancer DetectionKamilia Taguelmimt, Hong Phuong Dang, Gustavo Andrade-Miranda, Dimitris Visvikis, Bernard Malavaud, Julien Bert. 114-123 [doi]
- Generalized Polyp Detection from Colonoscopy Frames Using Proposed EDF-YOLO8 NetworkAlyaa Amer, Alaa Hussein, Noushin Ahmadvand, Sahar Magdy, Abas Abdi, Nasim Dadashi Serej, Noha Ghatwary, Neda Azarmehr. 124-132 [doi]
- AI-Assisted Laryngeal Examination SystemChiara Baldini, Muhammad Adeel Azam, Madelaine Thorniley, Claudio Sampieri, Alessandro Ioppi, Giorgio Peretti, Leonardo S. Mattos. 133-143 [doi]
- UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak SupervisionUfaq Khan, Umair Nawaz, Abdulmotaleb El-Saddik. 144-153 [doi]
- SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labelingLaurent Dillard, Hyeonsoo Lee, Weonsuk Lee, Tae-Soo Kim, Ali Diba, Thijs Kooi. 154-163 [doi]
- AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor PatientsRikhil Seshadri, Jayant Siva, Angelica Bartholomew, Clara Goebel, Gabriel Wallerstein-King, Beatriz López Morato, Nicholas Heller, Jason Scovell, Rebecca Campbell, Andrew Wood, Michal Ozery-Flato, Vesna Barros, Maria Gabrani, Michal Rosen-Zvi, Resha Tejpaul, Vidhyalakshmi Ramesh, Nikolaos Papanikolopoulos, Subodh Regmi, Ryan Ward, Robert Abouassaly, Steven C. Campbell, Erick Remer, Christopher Weight. 167-175 [doi]
- Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After SurgeryYuhan Zheng, Jessie A Elliott, John V. Reynolds, Sheraz R. Markar, Bartlomiej W. Papiez, ENSURE study group. 176-189 [doi]
- Treatment Efficacy Prediction of Focused Ultrasound Therapies Using Multi-parametric Magnetic Resonance ImagingAmanpreet Singh, Samuel I. Adams-Tew, Sara Johnson, Henrik Odéen, Jill Shea, Audrey Johnson, Lorena Day, Alissa Pessin, Allison Payne, Sarang C. Joshi. 190-199 [doi]
- SurRecNet: A Multi-task Model with Integrating MRI and Diagnostic Descriptions for Rectal Cancer Survival AnalysisRunqi Meng, Zonglin Liu, Yiqun Sun, Dengqiang Jia, Lin Teng, Qiong Ma, Tong Tong, Kaicong Sun, Dinggang Shen. 200-210 [doi]
- Improved Prediction of Recurrence After Prostate Cancer Radiotherapy Using Multimodal Data and in Silico simulationsValentin Septiers, Carlos Sosa Marrero, Renaud de Crevoisier, Aurélien Briens, Hilda Chourak, Maria A. Zuluaga, Oscar Acosta. 211-220 [doi]
- AutoDoseRank: Automated Dosimetry-Informed Segmentation Ranking for RadiotherapyZahira Mercado, Amith Kamath, Robert Poel, Jonas Willmann, Ekin Ermis, Elena Riggenbach, Lucas Mose, Nicolaus Andratschke, Mauricio Reyes 0001. 221-230 [doi]
- SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural NetworkMuhammad Ridzuan, Numan Saeed, Fadillah Adamsyah Maani, Karthik Nandakumar, Mohammad Yaqub. 231-240 [doi]