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Found 12 papers, 1 papers with code
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives
This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023.
Benchmarking Pretrained Vision Embeddings for Near- and Duplicate Detection in Medical Images
Near- and duplicate image detection is a critical concern in the field of medical imaging.
Medical Image Retrieval Using Pretrained Embeddings
A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging.
Uncertainty Estimation in Contrast-Enhanced MR Image Translation with Multi-Axis Fusion
In recent years, deep learning has been applied to a wide range of medical imaging and image processing tasks.
fRegGAN with K-space Loss Regularization for Medical Image Translation
Generative adversarial networks (GANs) have shown remarkable success in generating realistic images and are increasingly used in medical imaging for image-to-image translation tasks.
Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder
In this work, we investigate the impact of transfer learning for segmentation problems, being pixel-wise classification problems that can be tackled with encoder-decoder architectures.
Fused Audio Instance and Representation for Respiratory Disease Detection
Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an Area Under the Receiver Operating Characteristic Curve (AUC) score of 0. 8658, a sensitivity of 0. 8057, and a specificity of 0. 7958.
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?
In addition, we introduce Dynamic Visual Meta-Embedding (DVME) as an end-to-end transfer learning approach that fuses pretrained embeddings from multiple models.
Detecting genetic alterations in BRAF and NTRK as oncogenic drivers in digital pathology images: towards model generalization within and across multiple thyroid cohorts.
In this paper, we describe the machine learning problem of identifying different types of tumors based on digital pathology images.
Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans
A similar multi-scale dual pathway 3D CNN is trained to identify coronary artery endpoints for terminating the tracking process.
Continual Learning for Domain Adaptation in Chest X-ray Classification
Over the last years, Deep Learning has been successfully applied to a broad range of medical applications.
Deep Learning Based Rib Centerline Extraction and Labeling
In this paper, we combine a deep learning-based rib detection with a dedicated centerline extraction algorithm applied to the detection result for the purpose of fast, robust and accurate rib centerline extraction and labeling from CT volumes.
