The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art
This survey paper focuses on one of the current primary issues challenging data mining
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …
Tackling class imbalance in computer vision: a contemporary review
Class imbalance is a key issue affecting the performance of computer vision applications
such as medical image analysis, objection detection and recognition, image segmentation …
such as medical image analysis, objection detection and recognition, image segmentation …
Deep transfer with minority data augmentation for imbalanced breast cancer dataset
Clinical diagnosis of breast cancer is a challenging problem in the biomedical domain. The
BreakHis breast cancer histopathological image dataset consists of two classes: Benign …
BreakHis breast cancer histopathological image dataset consists of two classes: Benign …
Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets
Screening and diagnosis of diabetic retinopathy disease is a well known problem in the
biomedical domain. The use of medical imagery from a patient's eye for detecting the …
biomedical domain. The use of medical imagery from a patient's eye for detecting the …
Rib segmentation algorithm for X-ray image based on unpaired sample augmentation and multi-scale network
H Wang, D Zhang, S Ding, Z Gao, J Feng… - Neural Computing and …, 2023 - Springer
Rib segmentation based on chest X-ray images is essential in the computer-aided diagnosis
systems of lung cancer, which serves as an important step in the quantitative analysis of …
systems of lung cancer, which serves as an important step in the quantitative analysis of …
Vggin-net: Deep transfer network for imbalanced breast cancer dataset
In this paper, we have presented a novel deep neural network architecture involving transfer
learning approach, formed by freezing and concatenating all the layers till block4 pool layer …
learning approach, formed by freezing and concatenating all the layers till block4 pool layer …
Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning
Tumour heterogeneity in breast cancer poses challenges in predicting outcome and
response to therapy. Spatial transcriptomics technologies may address these challenges, as …
response to therapy. Spatial transcriptomics technologies may address these challenges, as …
Bag-of-Visual-Words codebook generation using deep features for effective classification of imbalanced multi-class image datasets
Classification of imbalanced multi-class image datasets is a challenging problem in
computer vision. Most of the real-world datasets are imbalanced in nature because of the …
computer vision. Most of the real-world datasets are imbalanced in nature because of the …
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines
Covid-19 has spread across the world and several vaccines have been developed to
counter its surge. To identify the correct sentiments associated with the vaccines from social …
counter its surge. To identify the correct sentiments associated with the vaccines from social …
Nucleotide augmentation for machine learning-guided protein engineering
Machine learning-guided protein engineering is a rapidly advancing field. Despite major
experimental and computational advances, collecting protein genotype (sequence) and …
experimental and computational advances, collecting protein genotype (sequence) and …