Organ Segmentation
91 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Organ Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Most implemented papers
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
abhi4ssj/few-shot-segmentation
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This representation is passed on to the segmenter arm that uses this information to segment the new query image.
Explainable multiple abnormality classification of chest CT volumes
rachellea/explainable-ct-ai
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We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images, in which a model must indicate the regions used to predict each abnormality.
Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation
lseventeen/flare22-twostagephtrans
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Abdominal organ segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
ljwztc/clip-driven-universal-model
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ICCV 2023
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
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.
Towards General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks
mohammedsb/dinov2forradiology
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To measure the effectiveness and generalizability of DINOv2's feature representations, we analyze the model across medical image analysis tasks including disease classification and organ segmentation on both 2D and 3D images, and under different settings like kNN, few-shot learning, linear-probing, end-to-end fine-tuning, and parameter-efficient fine-tuning.
Weighted Monte Carlo augmented spherical Fourier-Bessel convolutional layers for 3D abdominal organ segmentation
Filter-decomposition-based group equivariant convolutional neural networks show promising stability and data efficiency for 3D image feature extraction.
Hierarchical 3D fully convolutional networks for multi-organ segmentation
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models.
Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
MASILab/SSNet
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However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
The output imbalance refers to the imbalance between the false positives and false negatives of the inference model.
