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ORCID3rd CaPTion@MICCAI 2024: Marrakesh, Morocco
- Sharib Ali
, Fons van der Sommen, Bartlomiej Wladyslaw Papiez, Noha Ghatwary, Yueming Jin, Iris Kolenbrander:
Cancer Prevention, Detection, and Intervention - Third MICCAI Workshop, CaPTion 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. Lecture Notes in Computer Science 15199, Springer 2025, ISBN 978-3-031-73375-8
Classification and Characterization
- Cris 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:
Multi-center Ovarian Tumor Classification Using Hierarchical Transformer-Based Multiple-Instance Learning. 3-13 - Chenyi Hong, Hualiang Wang, Zhuoxuan Wu, Zuozhu Liu, Junhui Lv:
FoTNet Enables Preoperative Differentiation of Malignant Brain Tumors with Deep Learning. 14-25 - Aliza Subedi, Smriti Regmi, Nisha Regmi, Bhumi Bhusal, Ulas Bagci, Debesh Jha:
Classification of Endoscopy and Video Capsule Images Using CNN-Transformer Model. 26-36 - Tobias 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:
Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases. 37-47 - Eva Pachetti, Sara Colantonio:
Seeing More with Less: Meta-learning and Diffusion Models for Tumor Characterization in Low-Data Settings. 48-58 - Sadam 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:
Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification. 59-69
Detection and Segmentation
- Erlend Sortland Rolfsnes, Philip Thangngat, Trygve Eftestøl, Tobias Nordström, Fredrik Jäderling, Martin Eklund, Alvaro Fernandez-Quilez:
On Undesired Emergent Behaviors in Compound Prostate Cancer Detection Systems. 73-82 - Carolus 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:
Optimizing Multi-expert Consensus for Classification and Precise Localization of Barrett's Neoplasia. 83-92 - Krzysztof Kotowski, Bartosz Machura, Damian Kucharski, Benjamín Gutiérrez-Becker, Agata Krason, Jean Tessier, Jakub Nalepa:
Automated Hepatocellular Carcinoma Analysis in Multi-phase CT with Deep Learning. 93-103 - Sisi Yang, Alexandre Bône, Thomas Decaens, Joan Alexis Glaunès:
Refining Deep Learning Segmentation Maps with a Local Thresholding Approach: Application to Liver Surface Nodularity Quantification in CT. 104-113 - Kamilia Taguelmimt, Hong-Phuong Dang, Gustavo Andrade-Miranda, Dimitris Visvikis, Bernard Malavaud, Julien Bert:
Uncertainty-Aware Deep Learning Classification for MRI-Based Prostate Cancer Detection. 114-123 - Alyaa Amer, Alaa Hussein, Noushin Ahmadvand, Sahar Magdy, Abas Abdi, Nasim Dadashi Serej, Noha Ghatwary, Neda Azarmehr:
Generalized Polyp Detection from Colonoscopy Frames Using Proposed EDF-YOLO8 Network. 124-132 - Chiara Baldini, Muhammad Adeel Azam, Madelaine Thorniley, Claudio Sampieri, Alessandro Ioppi, Giorgio Peretti, Leonardo S. Mattos:
AI-Assisted Laryngeal Examination System. 133-143 - Ufaq Khan, Umair Nawaz, Abdulmotaleb El-Saddik:
UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision. 144-153 - Laurent Dillard, Hyeonsoo Lee, Weonsuk Lee, Tae Soo Kim, Ali Diba, Thijs Kooi:
SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labeling. 154-163
Cancer/Early Cancer Detection, Treatment, and Survival Prognosis
- Rikhil 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 J. Weight:
AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients. 167-175 - Yuhan Zheng, Jessie A Elliott, John V. Reynolds, Sheraz R. Markar, Bartlomiej W. Papiez, ENSURE study group:
Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery. 176-189 - Amanpreet Singh, Samuel I. Adams-Tew, Sara Johnson, Henrik Odéen, Jill Shea, Audrey Johnson, Lorena Day, Alissa Pessin, Allison Payne, Sarang C. Joshi:
Treatment Efficacy Prediction of Focused Ultrasound Therapies Using Multi-parametric Magnetic Resonance Imaging. 190-199 - Runqi Meng, Zonglin Liu, Yiqun Sun, Dengqiang Jia, Lin Teng, Qiong Ma, Tong Tong, Kaicong Sun, Dinggang Shen:
SurRecNet: A Multi-task Model with Integrating MRI and Diagnostic Descriptions for Rectal Cancer Survival Analysis. 200-210 - Valentin Septiers, Carlos Sosa Marrero, Renaud de Crevoisier, Aurélien Briens, Hilda Chourak, Maria A. Zuluaga, Oscar Acosta:
Improved Prediction of Recurrence After Prostate Cancer Radiotherapy Using Multimodal Data and in Silico simulations. 211-220 - Zahira Mercado, Amith Kamath, Robert Poel, Jonas Willmann, Ekin Ermis, Elena Riggenbach, Lucas Mose, Nicolaus Andratschke, Mauricio Reyes:
AutoDoseRank: Automated Dosimetry-Informed Segmentation Ranking for Radiotherapy. 221-230 - Muhammad Ridzuan, Numan Saeed, Fadillah Adamsyah Maani, Karthik Nandakumar, Mohammad Yaqub:
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network. 231-240
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