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Found 6 papers, 3 papers with code
Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review
This has sparked significant interest in applying LLMs to enhance various aspects of healthcare, ranging from medical education to clinical decision support.
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency.
Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization
Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant.
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning.
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis.
Composite Localization for Human Pose Estimation
This work proposes a novel deep learning framework for human pose estimation called composite localization to divide the complex learning objective into two simpler ones: a sparse heatmap to find the keypoint's approximate location and two short-distance offsetmaps to obtain its final precise coordinates.
