26 research outputs found
Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis
Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest
X-ray (CXR) images is critical. To reduce intra- and inter-observer
variability, during the radiological assessment, computer-aided diagnostic
tools have been utilized to supplement medical decision-making and subsequent
disease management. Computational methods with high accuracy and robustness are
required for rapid triaging of patients and aiding radiologists in the
interpretation of the collected data. In this study, we propose a novel
multi-feature fusion network using parallel attention blocks to fuse the
original CXR images and local-phase feature-enhanced CXR images at
multi-scales. We examine our model on various COVID-19 datasets acquired from
different organizations to assess the generalization ability. Our experiments
demonstrate that our method achieves state-of-art performance and has improved
generalization capability, which is crucial for widespread deployment.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2023:00
Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis
The role of chest X-ray (CXR) imaging, due to being more cost-effective,
widely available, and having a faster acquisition time compared to CT, has
evolved during the COVID-19 pandemic. To improve the diagnostic performance of
CXR imaging a growing number of studies have investigated whether supervised
deep learning methods can provide additional support. However, supervised
methods rely on a large number of labeled radiology images, which is a
time-consuming and complex procedure requiring expert clinician input. Due to
the relative scarcity of COVID-19 patient data and the costly labeling process,
self-supervised learning methods have gained momentum and has been proposed
achieving comparable results to fully supervised learning approaches. In this
work, we study the effectiveness of self-supervised learning in the context of
diagnosing COVID-19 disease from CXR images. We propose a multi-feature Vision
Transformer (ViT) guided architecture where we deploy a cross-attention
mechanism to learn information from both original CXR images and corresponding
enhanced local phase CXR images. We demonstrate the performance of the baseline
self-supervised learning models can be further improved by leveraging the local
phase-based enhanced CXR images. By using 10\% labeled CXR scans, the proposed
model achieves 91.10\% and 96.21\% overall accuracy tested on total 35,483 CXR
images of healthy (8,851), regular pneumonia (6,045), and COVID-19 (18,159)
scans and shows significant improvement over state-of-the-art techniques. Code
is available https://github.com/endiqq/Multi-Feature-ViTComment: Accepted to the 2022 MICCAI Workshop on Medical Image Learning with
Limited and Noisy Dat
Ambiguous Medical Image Segmentation using Diffusion Models
Collective insights from a group of experts have always proven to outperform
an individual's best diagnostic for clinical tasks. For the task of medical
image segmentation, existing research on AI-based alternatives focuses more on
developing models that can imitate the best individual rather than harnessing
the power of expert groups. In this paper, we introduce a single diffusion
model-based approach that produces multiple plausible outputs by learning a
distribution over group insights. Our proposed model generates a distribution
of segmentation masks by leveraging the inherent stochastic sampling process of
diffusion using only minimal additional learning. We demonstrate on three
different medical image modalities- CT, ultrasound, and MRI that our model is
capable of producing several possible variants while capturing the frequencies
of their occurrences. Comprehensive results show that our proposed approach
outperforms existing state-of-the-art ambiguous segmentation networks in terms
of accuracy while preserving naturally occurring variation. We also propose a
new metric to evaluate the diversity as well as the accuracy of segmentation
predictions that aligns with the interest of clinical practice of collective
insights