SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis
Data-driven methods have shown tremendous progress in medical image analysis. In this
context, deep learning-based supervised methods are widely popular. However, they …
context, deep learning-based supervised methods are widely popular. However, they …
Improving colonoscopy lesion classification using semi-supervised deep learning
While data-driven approaches excel at many image analysis tasks, the performance of these
approaches is often limited by a shortage of annotated data available for training. Recent …
approaches is often limited by a shortage of annotated data available for training. Recent …
Leveraging other datasets for medical imaging classification: evaluation of transfer, multi-task and semi-supervised learning
To address the data scarcity challenge in developing deep learning based medical imaging
classification, a widely-used strategy is to leverage other available datasets in training …
classification, a widely-used strategy is to leverage other available datasets in training …
Order-guided disentangled representation learning for ulcerative colitis classification with limited labels
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis,
involves two main difficulties. First, endoscopic images with the annotation about UC …
involves two main difficulties. First, endoscopic images with the annotation about UC …
[HTML][HTML] Improving image classification of gastrointestinal endoscopy using curriculum self-supervised learning
Endoscopy, a widely used medical procedure for examining the gastrointestinal (GI) tract to
detect potential disorders, poses challenges in manual diagnosis due to non-specific …
detect potential disorders, poses challenges in manual diagnosis due to non-specific …
Multi-module attention-guided deep learning framework for precise gastrointestinal disease identification in endoscopic imagery
The automated classification of gastrointestinal endoscopy images holds immense
importance in modern health care. It streamlines the diagnostic process by enabling faster …
importance in modern health care. It streamlines the diagnostic process by enabling faster …
CNNs vs. Transformers: Performance and Robustness in Endoscopic Image Analysis
In endoscopy, imaging conditions are often challenging due to organ movement, user
dependence, fluctuations in video quality and real-time processing, which pose …
dependence, fluctuations in video quality and real-time processing, which pose …
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …
analysis. However, despite these achievements, the further enhancement of deep learning …
From fully-supervised, single-task to scarcely-supervised, multi-task deep learning for medical image analysis
AAZ Imran - 2020 - dl.acm.org
Image analysis based on machine learning has gained prominence with the advent of deep
learning, particularly in medical imaging. To be effective in addressing challenging image …
learning, particularly in medical imaging. To be effective in addressing challenging image …
Unsupervised feature learning with K-means and an ensemble of deep convolutional neural networks for medical image classification
Medical image analysis using supervised deep learning methods remains problematic
because of the reliance of deep learning methods on large amounts of labelled training …
because of the reliance of deep learning methods on large amounts of labelled training …