Bennett A Landman

Latest

• Generalizing deep learning brain segmentation for skull removal and intracranial measurements
• Attention-Guided Supervised Contrastive Learning for Semantic Segmentation
• Body Part Regression With Self-Supervision
• Circle Representation for Medical Object Detection
• Compound Figure Separation of Biomedical Images with Side Loss
• Corrigendum to\" Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline\"[Comput. Biol. Med. 107 (2019) 109-117].
• Deep multi-path network integrating incomplete biomarker and chest CT data for evaluating lung cancer risk
• Default mode network connectivity and cognition in the aging brain: the effects of age, sex, and APOE genotype.
• Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking
• High-resolution 3D abdominal segmentation with random patch network fusion
• Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective
• Phase identification for dynamic CT enhancements with generative adversarial network
• Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging
• Rap-Net: Coarse-To-Fine Multi-Organ Segmentation With Single Random Anatomical Prior
• Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation
• Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT
• Validation and estimation of spleen volume via computer-assisted segmentation on clinically acquired CT scans
• A fully automated pipeline for brain structure segmentation in multiple sclerosis
• CircleNet: Anchor-free detection with circle representation
• Deep multi-task prediction of lung cancer and cancer-free progression from censored heterogenous clinical imaging
• Generalizing deep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning
• Harvesting, detecting, and characterizing liver lesions from large-scale multi-phase CT data via deep dynamic texture learning
• Internal-transfer weighting of multi-task learning for lung cancer detection
• Learning from dispersed manual annotations with an optimized data weighting policy
• Multiatlas segmentation
• Outlier guided optimization of abdominal segmentation
• Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records
• Semi-supervised Machine Learning with MixMatch and Equivalence Classes
• Semi-supervised multi-organ segmentation through quality assurance supervision
• The Value of Nullspace Tuning Using Partial Label Information
• Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
• Validation and optimization of multi-organ segmentation on clinical imaging archives
• 3D whole brain segmentation using spatially localized atlas network tiles
• An end to end automated pipeline for brain structure segmentation in multiple sclerosis patients
• Anatomical context improves deep learning on the brain age estimation task
• Cortical surface parcellation using spherical convolutional neural networks
• Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations
• Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks
• On-the-fly scheduling versus reservation-based scheduling for unpredictable workflows
• Adversarial synthesis learning enables segmentation without target modality ground truth
• Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks
• Synseg-net: Synthetic segmentation without target modality ground truth
• Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion
• Consistent cortical reconstruction and multi-atlas brain segmentation
• Multi-atlas learner fusion: An efficient segmentation approach for large-scale data