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Found 8 papers, 0 papers with code
Unsupervised augmentation optimization for few-shot medical image segmentation
The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples.
GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision
To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner.
FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification
Deep learning is becoming increasingly ubiquitous in medical research and applications while involving sensitive information and even critical diagnosis decisions.
MURPHY: Relations Matter in Surgical Workflow Analysis
Autonomous robotic surgery has advanced significantly based on analysis of visual and temporal cues in surgical workflow, but relational cues from domain knowledge remain under investigation.
Active CT Reconstruction with a Learned Sampling Policy
Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.
3D endoscopic depth estimation using 3D surface-aware constraints
We propose a loss function for depth estimation that integrates the surface-aware constraints, leading to a faster and better convergence with the valid information from spatial information.
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging.
Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT
Machine translation (MT) draws from several different disciplines, making it a complex subject to teach.
