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09/02/2023
Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistency
The task of shape abstraction with semantic part consistency is challeng...
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06/04/2023
Training Like a Medical Resident: Universal Medical Image Segmentation via Context Prior Learning
A major enduring focus of clinical workflows is disease analytics and di...
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10/10/2022
Visual Prompt Tuning for Test-time Domain Adaptation
Models should have the ability to adapt to unseen data during test-time ...
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03/21/2022
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers
Combining information from multi-view images is crucial to improve the p...
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02/28/2022
A Multi-scale Transformer for Medical Image Segmentation: Architectures, Model Efficiency, and Benchmarks
Transformers have emerged to be successful in a number of natural langua...
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01/22/2022
Modality Bank: Learn multi-modality images across data centers without sharing medical data
Multi-modality images have been widely used and provide comprehensive in...
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07/02/2021
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
Transformer architecture has emerged to be successful in a number of nat...
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04/05/2021
FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images
Radiotherapy is a treatment where radiation is used to eliminate cancer ...
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03/30/2021
Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
Data augmentation has proved extremely useful by increasing training dat...
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02/04/2021
SelfNorm and CrossNorm for Out-of-Distribution Robustness
Normalization techniques are crucial in stabilizing and accelerating the...
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07/17/2020
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Data augmentation is one of the most important tools in training modern ...
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07/28/2019
