Search Results for author:
Found 21 papers, 2 papers with code
Nodule detection and generation on chest X-rays: NODE21 Challenge
To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays.
A systematic approach to deep learning-based nodule detection in chest radiographs
We illustrate how this analysis and a combination of multiple architectures results in state-of-the-art performance for lung nodule detection, which is demonstrated by the proposed model winning the detection track of the Node21 competition.
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus
Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability.
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data
Overall, we highlight the importance of clean data sets for UAD in brain MRI and demonstrate an approach for detecting falsely labeled data directly during training.
Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning
The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5. 01+-4. 37 kPa.
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction
We analyze the value of age information during training, as an additional anomaly score, and systematically study several architecture concepts.
Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning
Our top-performing method achieves the AUC-ROC value of 90. 90\% compared to 84. 53\% using the previous approach with smaller input tiles.
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI
These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning.
Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets
In this work, we propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.
Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification
We analyze the value of using multiple hyperspectral bands compared to conventional RGB images and we study several machine learning models' ability to make use of the additional spectral information.
4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes
Tracking and localizing objects is a central problem in computer-assisted surgery.
Deep learning with 4D spatio-temporal data representations for OCT-based force estimation
Moreover, optical coherence tomography (OCT) and deep learning have been used for estimating forces based on deformation observed in volumetric image data.
Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data
For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach.
A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
We propose 4D spatio-temporal deep learning for end-to-end motion forecasting and estimation using a stream of OCT volumes.
Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
A recent study has shown that hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors, as precancerous or cancerous lesions show specific spectral signatures that distinguish them from healthy tissue.
4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification
Autism spectrum disorder (ASD) is associated with behavioral and communication problems.
4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation
While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently.
4D Deep Learning for Multiple-Sclerosis Lesion Activity Segmentation
While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently.
Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models
As a result, we propose a recurrent model with state-max-pooling which automatically learns the relevance of different EIS measurements.
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels
To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup.
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images
For feedback during interventions, real-time in-vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation.
