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Found 12 papers, 1 papers with code
Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images
Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications.
Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays
Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data.
Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?
Literature is sparse in discussing the optimal image resolution to train these models for segmenting the Tuberculosis (TB)-consistent lesions in CXRs.
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles.
Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs
We observed that the stacking ensemble demonstrated superior segmentation performance (Dice score: 0. 5743, 95% confidence interval: (0. 4055, 0. 7431)) compared to the individual constituent models and other ensemble methods.
Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets
We believe that the present research shows a novel direction in developing criteria-specific custom deep models for cervical image classification by combining images from different sources unlabeled and/or labeled with varying criteria, and addressing image access restrictions.
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Histopathological analysis is the present gold standard for precancerous lesion diagnosis.
Synthetic Sample Selection via Reinforcement Learning
However, the quality control of synthetic images for data augmentation purposes is under-investigated, and some of the generated images are not realistic and may contain misleading features that distort data distribution when mixed with real images.
Feature based Sequential Classifier with Attention Mechanism
Cervical cancer is one of the deadliest cancers affecting women globally.
Selective Synthetic Augmentation with Quality Assurance
To mitigate these issues, we investigate a novel data augmentation pipeline that selectively adds new synthetic images generated by conditional Adversarial Networks (cGANs), rather than extending directly the training set with synthetic images.
Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data.
Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification
Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations.
