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Found 9 papers, 5 papers with code
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github. com/uni-medical/IMIS-Bench.
SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
Supervised Finetuning (SFT) serves as an effective way to adapt such foundation models for task-specific downstream tasks but at the cost of degrading the general knowledge previously stored in the original foundation model. To address this, we propose SAM-Med3D-MoE, a novel framework that seamlessly integrates task-specific finetuned models with the foundational model, creating a unified model at minimal additional training expense for an extra gating network.
SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes.
SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
In this paper, we introduce SAM-Med3D for general-purpose segmentation on volumetric medical images.
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation
To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.
SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks
Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure.
PL-Net: Progressive Learning Network for Medical Image Segmentation
In recent years, segmentation methods based on deep convolutional neural networks (CNNs) have made state-of-the-art achievements for many medical analysis tasks.
Deep learning-based person re-identification methods: A survey and outlook of recent works
In recent years, with the increasing demand for public safety and the rapid development of intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot research topics in the computer vision field.
