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Found 15 papers, 9 papers with code
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
This paper proposes a Context Axial Reverse Attention Network (CaraNet) to improve the segmentation performance on small objects compared with several recent state-of-the-art models.
Informing selection of performance metrics for medical image segmentation evaluation using configurable synthetic errors
By analyzing the intrinsic properties of these metrics and categorizing the segmentation errors, we are working toward the goal of developing a decision-tree tool for assisting in the selection of segmentation performance metrics.
A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers
Instead of using current deep-learning segmentation models (like the UNet and variants), we approach the segmentation problem using trained Convolutional Neural Network (CNN) classifiers, which automatically extract important features from images for classification.
A Novel Intrinsic Measure of Data Separability
To quantitatively measure the separability of datasets, we create an intrinsic measure -- the Distance-based Separability Index (DSI), which is independent of the classifier model.
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
Segmenting medical images accurately and reliably is important for disease diagnosis and treatment.
Ranked #9 on
Medical Image Segmentation
on ETIS-LARIBPOLYPDB
A Distance-based Separability Measure for Internal Cluster Validation
Without true labels, to design an effective CVI is as difficult as to create a clustering method.
CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation
By comparison, CFPNet-M achieves comparable segmentation results on all five medical datasets with only 0. 65 million parameters, which is about 2% of U-Net, and 8. 8 MB memory.
Understanding the Ability of Deep Neural Networks to Count Connected Components in Images
We proposed three ML-learnable characteristics to verify learnable problems for ML models, such as DNNs, and explain why DNNs work for specific counting problems but cannot generally count connected components.
Segmentation of Infrared Breast Images Using MultiResUnet Neural Network
It was used to segment the breast area by using a set of breast IR images, collected in our pilot study by imaging breast cancer patients and normal volunteers with a thermal infrared camera (N2 Imager).
The training accuracy of two-layer neural networks: its estimation and understanding using random datasets
In this study, we propose a novel theory based on space partitioning to estimate the approximate training accuracy for two-layer neural networks on random datasets without training.
Analysis of Generalizability of Deep Neural Networks Based on the Complexity of Decision Boundary
We create the decision boundary complexity (DBC) score to define and measure the complexity of decision boundary of DNNs.
An Internal Cluster Validity Index Using a Distance-based Separability Measure
And, to have more CVIs is crucial because there is no universal CVI that can be used to measure all datasets, and no specific method for selecting a proper CVI for clusters without true labels.
DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation
The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especially, for medical images.
Data Separability for Neural Network Classifiers and the Development of a Separability Index
In machine learning, the performance of a classifier depends on both the classifier model and the dataset.
A Novel Measure to Evaluate Generative Adversarial Networks Based on Direct Analysis of Generated Images
We characterize the performance of a GAN as an image generator according to three aspects: 1) Creativity: non-duplication of the real images.
