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Found 10 papers, 4 papers with code
Slide-SAM: Medical SAM Meets Sliding Window
To address these problems, we propose Slide-SAM, which treats a stack of three adjacent slices as a prediction window.
UOD: Universal One-shot Detection of Anatomical Landmarks
However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data.
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.
PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement
The pelvis, the lower part of the trunk, supports and balances the trunk.
DATR: Domain-adaptive transformer for multi-domain landmark detection
In this work, we propose a universal model for multi-domain landmark detection by taking advantage of transformer for modeling long dependencies and develop a domain-adaptive transformer model, named as DATR, which is trained on multiple mixed datasets from different anatomies and capable of detecting landmarks of any image from those anatomies.
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.
Relative distance matters for one-shot landmark detection
Contrastive learning based methods such as cascade comparing to detect (CC2D) have shown great potential for one-shot medical landmark detection.
You Only Learn Once: Universal Anatomical Landmark Detection
However, all of those methods are unary in the sense that a highly specialized network is trained for a single task say associated with a particular anatomical region.
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.
A$^3$DSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation
In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations.
