3D Medical Imaging Segmentation
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Benchmarks
TCIA Pancreas-CT
Most implemented
UNETR: Transformers for 3D Medical Image Segmentation
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
Med3D: Transfer Learning for 3D Medical Image Analysis
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Papers
3D Medical Imaging Segmentation on Non-Contrast CT
This technical report analyzes non-contrast CT image segmentation in computer vision. It revisits a proposed method, examines the background of non-contrast CT imaging, and highlights the significance of segmentation. Th…
3D Medical Imaging SegmentationImage SegmentationMedical Image SegmentationSegmentation+1A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation
Segmentation of 3D medical images is a critical task for accurate diagnosis and treatment planning. Convolutional neural networks (CNNs) have dominated the field, achieving significant success in 3D medical image segment…
3D Medical Imaging SegmentationDomain AdaptationImage SegmentationMedical Image Segmentation+2LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models
The widespread adoption of large-scale pre-training techniques has significantly advanced the development of medical foundation models, enabling them to serve as versatile tools across a broad range of medical tasks. How…
3D Medical Imaging SegmentationMedical Image ClassificationMIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of standardized tools for training, testing, …
3D Medical Imaging SegmentationMedical Image SegmentationSegmentationxLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart
Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) have been pivotal in biomedical image segmentation, yet their ability to manage long-range dependencies remains constrained by inherent locality and comp…
3D Medical Imaging Segmentationimage-classificationImage ClassificationImage Segmentation+5FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly prompted. Interactivity is a key strength of …
3D Medical Imaging SegmentationGPUMedical Image SegmentationTransfer Learning+1