221 results on '"Jose Dolz"'
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52. Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision.
53. Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.
54. Source-Relaxed Domain Adaptation for Image Segmentation.
55. Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation.
56. Laplacian Regularized Few-Shot Learning.
57. Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images.
58. Multi-Scale Self-Guided Attention for Medical Image Segmentation.
59. Self-Learning for Weakly Supervised Gleason Grading of Local Patterns.
60. Constrained unsupervised anomaly segmentation.
61. Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images.
62. Mutual Information-based Generalized Category Discovery.
63. Class Adaptive Network Calibration.
64. A Strong Baseline for Generalized Few-Shot Semantic Segmentation.
65. Calibrating Segmentation Networks with Margin-based Label Smoothing.
66. Segmentation with mixed supervision: Confidence maximization helps knowledge distillation.
67. Calibrating segmentation networks with margin-based label smoothing.
68. Boundary loss for highly unbalanced segmentation.
69. Constrained Domain Adaptation for Segmentation.
70. Curriculum Semi-supervised Segmentation.
71. Discretely-constrained deep network for weakly supervised segmentation.
72. Isointense infant brain segmentation with a hyper-dense connected convolutional neural network.
73. IVD-Net: Intervertebral Disc Localization and Segmentation in MRI with a Multi-modal UNet.
74. Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities.
75. An Attention Model for Group-Level Emotion Recognition.
76. Information Maximization for Few-Shot Learning.
77. Looking at the whole picture: constrained unsupervised anomaly segmentation.
78. Source-Free Domain Adaptation for Image Segmentation.
79. Weakly supervised segmentation with cross-modality equivariant constraints.
80. Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains.
81. Mutual-Information Based Few-Shot Classification.
82. Transductive Few-Shot Learning: Clustering is All You Need?
83. The hidden label-marginal biases of segmentation losses.
84. The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration.
85. Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer.
86. Mixed-supervised segmentation: Confidence maximization helps knowledge distillation.
87. Bladder segmentation based on deep learning approaches: current limitations and lessons.
88. Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge.
89. HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation.
90. Constrained-CNN losses for weakly supervised segmentation.
91. Weakly supervised segmentation with cross-modality equivariant constraints.
92. Source-free domain adaptation for image segmentation.
93. Incremental multi-target domain adaptation for object detection with efficient domain transfer.
94. DOPE: Distributed Optimization for Pairwise Energies.
95. Unbiased Shape Compactness for Segmentation.
96. Privacy Preserving for Medical Image Analysis via Non-Linear Deformation Proxy.
97. Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.
98. 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.
99. Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
100. Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty.
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