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Found 61 papers, 13 papers with code
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection
Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers.
A Calibration Approach for Elasticity Estimation with Medical Tools
By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired.
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images.
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection
We demonstrate that this ensembling strategy can enhance the performance of DMs and mitigate the sensitivity to different kernel sizes across varying pathologies, highlighting its promise for brain MRI anomaly detection.
A Modified da Vinci Surgical Instrument for OCE based Elasticity Estimation with Deep Learning
We propose modifying a da~Vinci surgical instrument to realize optical coherence elastography (OCE) for quantitative elasticity estimation.
PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM
Our primary novelty lies in PolypNextLSTM, which stands out as the leanest in parameters and the fastest model, surpassing the performance of five state-of-the-art image and video-based deep learning models.
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.
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
Using our proposed conditioning mechanism we can reduce the false-positive predictions and enable a more precise delineation of anomalies which significantly enhances the anomaly detection performance compared to established state-of-the-art approaches to unsupervised anomaly detection in brain MRI.
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps.
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.
Collaborative Robotic Biopsy with Trajectory Guidance and Needle Tip Force Feedback
Robotic trajectory guidance has been shown to improve needle positioning, but feedback for real-time navigation is limited.
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
Needle positioning is essential for various medical applications such as epidural anaesthesia.
Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus
We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS.
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples.
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.
Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs
Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
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.
Modeling R$^3$ Needle Steering in Uppaal
Medical cyber-physical systems are safety-critical, and as such, require ongoing verification of their correct behavior, as system failure during run time may cause severe (or even fatal) personal damage.
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
In POTOBIM, we optimize both the parameters of the prior distribution and the posterior temperature with respect to reconstruction accuracy using Bayesian optimization with Gaussian process regression.
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.
Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles
Autonomous systems are often applied in uncertain environments, which require prospective action planning and retrospective data evaluation for future planning to ensure safe operation.
Self-Supervised U-Net for Segmenting Flat and Sessile Polyps
This is followed by a supervised training on the limited Kvasir-Sessile dataset.
A novel optical needle probe for deep learning-based tissue elasticity characterization
The distinction between malignant and benign tumors is essential to the treatment of cancer.
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.
Posterior Temperature Optimization in Variational Inference for Inverse Problems
Bayesian methods feature useful properties for solving inverse problems, such as tomographic reconstruction.
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.
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 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.
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 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.
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.
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.
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.
Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery
The resulting volumentric images provide additional information on the shape of caveties in the bone structure, which will be useful for image-to-patient registration and to estimate the drill trajectory.
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.
Cancer Classification
Colon Cancer Detection In Confocal Laser Microscopy Images
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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.
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.
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.
