Pneumonia Detection with Semantic Similarity Scores
Gholamipoor, r.; Rafiee, N.; Kollmann, M.
Show abstract
X-ray images have been widely used for medical diagnoses of cardiothoracic and pulmonary abnormalities due to its noninvasiveness. Advancement in computer-aided diagnostic technologies, such as deep supervised methods, can help radiologists with a reliable early treatment and reduce diagnosis time. Nevertheless, these methods are prone to the small number of labeled samples and are limited to a specific abnormality. In this paper we combined a self-supervised contrastive method with a Mahalanobis distance score to develope an abnormality detection method that uses only healthy images during the training procedure. We were able to outperform previous unsupervised methods for the task of Pneumonia detection. We show that representation learned by the self-supervised method improves the supervised tasks for Pneumonia detection.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SN-FPN: Self-attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation 96%
- Cell segmentation without annotation by unsupervised domain adaptation based on cooperative self-learning 94%
- The tempest in a cubic millimeter: Image-based refinements necessitate the reconstruction of 3D microvasculature from a large series of damaged alternately-stained histological sections 94%
Similar papers in this journal
- Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes 96%
- An Assistive Computer Vision Tool to Automatically Detect Changes in Fish Behavior In Response to Ambient Odor 95%
- A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks 95%
Similar papers in this journal
- Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement 96%
- Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet 95%
- ai-corona : Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.