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Found 18 papers, 4 papers with code
Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification
Firstly, unlike current methods that usually employ two individual models for for clinical and dermoscopy modalities, we verified that multimodal feature can be learned by sharing the parameters of encoder while leaving the individual modal-specific classifiers.
Sparsity-based background removal for STORM super-resolution images
We introduce a sparsity-based background removal method by adapting a neural network (SLNet) from a different microscopy domain.
Joint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification
Most convolutional neural network (CNN) based methods for skin cancer classification obtain their results using only dermatological images.
SR-R$^2$KAC: Improving Single Image Defocus Deblurring
To further alleviate the contingent effect of recursive stacking, i. e., ringing artifacts, we add identity shortcuts between atrous convolutions to simulate residual deconvolutions.
Improving image quality of sparse-view lung tumor CT images with U-Net
Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views.
Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images
However, most methods only focus on designing a better module for multi-modal data fusion; few methods explore utilizing the label correlation between SPC and skin disease for performance improvement.
Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows
Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring.
Higher Chest X-ray Resolution Improves Classification Performance
Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons.
WindowNet: Learnable Windows for Chest X-ray Classification
Finally, we propose and evaluate WindowNet, a model that learns optimal window settings, and show that it significantly improves performance compared to the baseline model without windowing.
Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler.
German CheXpert Chest X-ray Radiology Report Labeler
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports.
Transformer-based interpretable multi-modal data fusion for skin lesion classification
A lot of deep learning (DL) research these days is mainly focused on improving quantitative metrics regardless of other factors.
Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction
Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases.
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0. 86 [0. 82, 0. 89].
Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification
Conclusion: ViTs performed similarly to CNNs in CXR classification, and their attention-based saliency maps were more useful to radiologists and outperformed GradCAM.
A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification
The proposed IDV approach trained on ID (chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0. 999 OOD AUC across the three data sets, surpassing all other OOD detection methods.
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
We investigate the performance of the network on sparse-view chest CT scans, and we highlight the added benefit of having a trainable reconstruction layer over the more conventional fixed ones.
A Gentle Introduction to Deep Learning in Medical Image Processing
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications.
