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Found 7 papers, 2 papers with code
Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
For predicting cancer survival outcomes, standard approaches in clinical research are often based on two main modalities: pathology images for observing cell morphology features, and genomic (e. g., bulk RNA-seq) for quantifying gene expressions.
SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images.
ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic Cartilage Segmentation
Experiments on the mice cartilage dataset show the superiority of our new model compared to other competitive segmentation models.
SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings
Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions.
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images
Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard.
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations.
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning
High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance.
