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Found 12 papers, 6 papers with code
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies
OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications.
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
These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information.
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.
SAM-Med2D
To bridge this gap, we introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
However, the state-of-the-art models for medical image segmentation are still small-scale, with their parameters only in the tens of millions.
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results.
IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition
This method, named IntraLoss, explicitly performs gradient enhancement in the anisotropic region so that the intra-class distribution continues to shrink, resulting in isotropic and more compact intra-class distribution and further margin between identities.
TTPP: Temporal Transformer with Progressive Prediction for Efficient Action Anticipation
Video action anticipation aims to predict future action categories from observed frames.
A Discriminative Learned CNN Embedding for Remote Sensing Image Scene Classification
Concretely, for remote sensing image scene classification, we would like to map images from the same scene to feature vectors that are close, and map images from different scenes to feature vectors that are widely separated.
The General Pair-based Weighting Loss for Deep Metric Learning
A large amount of loss functions based on pair distances have been presented in the literature for guiding the training of deep metric learning.
