default search action
2nd MILLanD@MICCAI 2023: Vancouver, BC, Canada
- Zhiyun Xue
, Sameer K. Antani
Medical Image Learning with Limited and Noisy Data - Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. Lecture Notes in Computer Science 14307, Springer 2023, ISBN 978-3-031-47196-4
Efficient Annotation and Training Strategies
- Serban Vadineanu
Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. 3-13 - Yijie Qu, Qianfei Zhao, Linda Wei, Tao Lu, Shaoting Zhang, Guotai Wang:
ScribSD: Scribble-Supervised Fetal MRI Segmentation Based on Simultaneous Feature and Prediction Self-distillation. 14-23 - Nazanin Moradinasab
Label-Efficient Contrastive Learning-Based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images. 24-34 - Christopher Adnel, Islem Rekik
Affordable Graph Neural Network Framework Using Topological Graph Contraction. 35-46
Approaches for Noisy, Missing, and Low Quality Data
- Xiongchao Chen, Bo Zhou, Huidong Xie
Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT. 49-59 - Yang Yu
A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging. 60-70 - Maryna Kvasnytsia
COVID-19 Lesion Segmentation Framework for the Contrast-Enhanced CT in the Absence of Contrast-Enhanced CT Annotations. 71-81 - Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Joseph T. Roland, Ken S. Lau, Qi Liu, Lori A. Coburn, Keith T. Wilson
Feasibility of Universal Anomaly Detection Without Knowing the Abnormality in Medical Images. 82-92
Unsupervised, Self-supervised, and Contrastive Learning
- Camille Ruppli, Pietro Gori, Roberto Ardon, Isabelle Bloch:
Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection. 95-105 - Yunsung Chung, Chanho Lim, Chao Huang, Nassir Marrouche, Jihun Hamm:
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation. 106-116 - Tony Xu
Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy. 117-127 - Zhaohui Liang
Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning. 128-137 - Rahul G. S., Sriprabha Ramanarayanan, Mohammad Al Fahim
SDLFormer: A Sparse and Dense Locality-Enhanced Transformer for Accelerated MR Image Reconstruction. 138-147 - Slim Hachicha, Célia Le, Valentine Wargnier-Dauchelle, Michaël Sdika:
Robust Unsupervised Image to Template Registration Without Image Similarity Loss. 148-157 - Jianfei Liu, Omid Shafaat, Ronald M. Summers:
A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue. 158-167
Weakly-Supervised, Semi-supervised, and Multitask Learning
- Sajith Rajapaksa, Khashayar Namdar, Farzad Khalvati:
Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification. 171-180 - Ziyang Wang, Irina Voiculescu:
Exigent Examiner and Mean Teacher: An Advanced 3D CNN-Based Semi-Supervised Brain Tumor Segmentation Framework. 181-190 - Peidi Xu
Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation. 191-201 - Krishna Thoriya, Preeti Mutreja, Sumit Kalra, Angshuman Paul:
Multi-task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Images. 202-210
Active Learning
- Bella Specktor-Fadida
Test-Time Augmentation-Based Active Learning and Self-training for Label-Efficient Segmentation. 213-223 - Ji Wu, Zhongfeng Kang, Sebastian Nørgaard Llambias, Mostafa Mehdipour-Ghazi
Active Transfer Learning for 3D Hippocampus Segmentation. 224-234
Transfer Learning
- Gino Gulamhussene
Using Training Samples as Transitive Information Bridges in Predicted 4D MRI. 237-245 - Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian:
To Pretrain or Not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology. 246-256 - Masakata Kawai
Large-Scale Pretraining on Pathological Images for Fine-Tuning of Small Pathological Benchmarks. 257-267
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-12-22 18:46 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: