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Found 10 papers, 2 papers with code
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results.
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision.
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision.
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
We reformulate COPD binary classification as an anomaly detection task, proposing cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions.
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Given an ensemble of independently trained models, this results in correlated predictions and common failure modes.
Taming Detection Transformers for Medical Object Detection
The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures.
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting.
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance.
Extending nnU-Net is all you need
Semantic segmentation is one of the most popular research areas in medical image computing.
Temporal Feature Networks for CNN based Object Detection
For reliable environment perception, the use of temporal information is essential in some situations.
