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Found 4 papers, 1 papers with code
Embarrassingly Simple Scribble Supervision for 3D Medical Segmentation
We relate this shortcoming to two major issues: 1) the complex nature of many methods which deeply ties them to the underlying segmentation model, thus preventing a migration to more powerful state-of-the-art models as the field progresses and 2) the lack of a systematic evaluation to validate consistent performance across the broader medical domain, resulting in a lack of trust when applying these methods to new segmentation problems.
TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
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.
De Rham compatible Deep Neural Network FEM
Our construction and DNN architecture generalizes previous results in that no geometric restrictions on the regular simplicial partitions $\mathcal{T}$ of $\Omega$ are required for DNN emulation.
