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Found 26 papers, 8 papers with code
Fine-tuning BERT Models for Summarizing German Radiology Findings
Writing the conclusion section of radiology reports is essential for communicating the radiology findings and its assessment to physician in a condensed form.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
Open-Source Skull Reconstruction with MONAI
The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework.
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
Using this map, the change in tumor volume can be evaluated.
Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology
The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely.
Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding
To exploit anatomical structures in this scenario, we present a sophisticated automatic pipeline to gather and integrate human bodily structures from computed tomography datasets, which we incorporate in our PAXRay: A Projected dataset for the segmentation of Anatomical structures in X-Ray data.
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder
The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $\beta$-VAE to achieve a balance between the two losses is a tricky and dataset-specific task.
The HoloLens in Medicine: A systematic Review and Taxonomy
The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research.
AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier
Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy.
GAN-based generation of realistic 3D data: A systematic review and taxonomy
A solution to these problems can be the generation of synthetic data to perform data augmentation in combination with other more traditional methods of data augmentation.
Metrics reloaded: Pitfalls and recommendations for image analysis validation
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem.
k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Results: Both datasets were very similar to the ground truth (DICE scores of 92\%-98\% and Hausdorff distances of under 5. 5 mm).
Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training
We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting.
Ranked #1 on
Thoracic Disease Classification
on ChestX-ray14
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
We evaluate the SSM on several cranial implant design tasks, and the results show that, while the SSM performs suboptimally on synthetic defects compared to CNN-based approaches, it is capable of reconstructing large and complex defects with only minor manual corrections.
Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
Encouraging the latent representation of a generative model to be disentangled offers new perspectives of control and interpretability.
Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation
Following this thought, we use a small number of labeled images as reference material and match pixels in an unlabeled image to the semantics of the best fitting pixel in a reference set.
MOMO -- Deep Learning-driven classification of external DICOM studies for PACS archivation
Additionally, an ablation study was performed to measure the performance impact of the network ensemble in the algorithm, and a comparative performance test with a commercial product was conducted.
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if combined with a contrast agent, resulting in a CT angiography (CTA).
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
A Relational-learning Perspective to Multi-label Chest X-ray Classification
Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i. e. learning to map an image directly to its binary labels.
Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism
Detector-based spectral computed tomography is a recent dual-energy CT (DECT) technology that offers the possibility of obtaining spectral information.
Medical Deep Learning -- A systematic Meta-Review
With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information.
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs.
