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Found 17 papers, 9 papers with code
Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning
In this work, we investigate relatively less explored semi-supervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially.
UT-Net: Combining U-Net and Transformer for Joint Optic Disc and Cup Segmentation and Glaucoma Detection
The proposed model has been implemented for joint OD and OC segmentation on three publicly available datasets: DRISHTI-GS, RIM-ONE R3, and REFUGE.
Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation
Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images.
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task.
Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI
Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes.
MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation
Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis.
Ranked #1 on
Semantic Segmentation
on PH2
An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation
The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks.
SVM and ANN based Classification of EMG signals by using PCA and LDA
One of the main methods used for pattern recognition in myoelectric signals is the Support Vector Machines (SVM) technique whose primary function is to identify an n-dimensional hyperplane to separate a set of input feature points into different classes.
Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification
Cervical cancer is the fourth most common category of cancer, affecting more than 500, 000 women annually, owing to the slow detection procedure.
RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection
Swarm optimization algorithms are widely used for feature selection before data mining and machine learning applications.
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-Scans
COVID‑19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people.
Ranked #1 on
Image Classification
on SARS-COV-2
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection
A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the Grey Wolf Optimizer, thus improving the classification performance.
Ranked #1 on
Image Classification
on SIPaKMeD
DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation
The rapid increment of morbidity of brain stroke in the last few years have been a driving force towards fast and accurate segmentation of stroke lesions from brain MRI images.
Single Image Super-Resolution using Residual Channel Attention Network
In this paper, we propose a deep learning-based approach for the problem, wherein we use a fully convolutional attention network coupled with residual in the residual block (RIR), Residual Channel Attention Block (RCAB), and long and short skip connections.
Comparative study of maturation profiles of neural cells in different species with the help of Computer Vision and Deep Learning
Our results show different migration patterns between species suggesting different maturation profiles as well as different maturation rates of neuroblast cells in different species.
Optimizing Speech Emotion Recognition using Manta-Ray Based Feature Selection
Recent studies have shown that Mel Frequency Cepstral Coefficients (MFCC) have been emerged as one of the most relied feature extraction methods, though it circumscribes the accuracy of classification with a very small feature dimension.
Multi-scale Attention U-Net (MsAUNet): A Modified U-Net Architecture for Scene Segmentation
Also, these networks fail to map the long-range dependencies of local features, which results in discriminative feature maps corresponding to each semantic class in the resulting segmented image.
