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Found 31 papers, 4 papers with code
Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs
However, for monitoring disease progression, \textit{lesion activity} in terms of new and enlarging lesions between two time points is a crucial biomarker.
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.
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.
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.
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.
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.
Learning Preference-Based Similarities from Face Images using Siamese Multi-Task CNNs
The ground-truth for the similarity matching scores is determined by a test that aims to capture users' preferences, interests, and attitude that are relevant for forming romantic relationships.
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.
Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data
On the official test set our method is ranked first for both tasks with a balanced accuracy of 63. 6% for task 1 and 63. 4% for task 2.
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.
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
We compare 2D spatial and 3D spatio-temporal CNNs for LV indices regression and cardiac phase classification.
Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion
Our novel convGRU-CNN architecture results in the lowest mean absolute error of 1. 59 +- 1. 3 mN and a cross-correlation coefficient of 0. 9997, and clearly outperforms the other methods.
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.
Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation
We propose an efficient approach for NAS in the context of medical, image-based deep learning problems by searching for architectures on low-dimensional data which are subsequently transferred to high-dimensional data.
Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting
The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification.
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
As the results were promising, automatic classification of lesions might be feasible which could assist experts in their decision making.
Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks
Histological evaluation of tissue samples is a typical approach to identify colorectal cancer metastases in the peritoneum.
Colon Cancer Detection In Confocal Laser Microscopy Images
General Classification
+1
Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation
Optical coherence tomography (OCT) is an imaging modality which is used in interventions due to its high spatial resolution of few micrometers and its temporal resolution of potentially several hundred volumes per second.
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization
Typically, the scaffold is manually reviewed by an expert, analyzing each of the hundreds of image slices.
Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks
Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible.
Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting
We identify heavy class imbalance as a key problem for this challenge and consider multiple balancing approaches such as loss weighting and balanced batch sampling.
Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks
Head pose estimation and tracking is useful in variety of medical applications.
Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture
Needle insertion is common during minimally invasive interventions such as biopsy or brachytherapy.
Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification
Deep learning methods have shown impressive results for a variety of medical problems over the last few years.
Force Estimation from OCT Volumes using 3D CNNs
Our novel Siamese 3D CNN architecture outperforms single-path methods that achieve a mean average error of 11. 59 +- 6. 7 mN.
Plaque Classification in Coronary Arteries from IVOCT Images Using Convolutional Neural Networks and Transfer Learning
Advanced atherosclerosis in the coronary arteries is one of the leading causes of deaths worldwide while being preventable and treatable.
A Deep Learning Approach for Pose Estimation from Volumetric OCT Data
We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes.
