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Found 38 papers, 21 papers with code
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting.
MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
Our findings offer a new direction for the medical imaging community to effectively utilize the wealth of available data for improved segmentation performance.
Accurate Detection of Mediastinal Lesions with nnDetection
The accurate detection of mediastinal lesions is one of the rarely explored medical object detection problems.
[Work in progress] Scalable, out-of-the box segmentation of individual particles from mineral samples acquired with micro CT
Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, makes use of a border-core representation to enable instance segmentation and is trained with a large dataset containing particles of numerous different materials and minerals.
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
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.
Extending nnU-Net is all you need
Semantic segmentation is one of the most popular research areas in medical image computing.
Continuous-Time Deep Glioma Growth Models
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.
nnDetection: A Self-configuring Method for Medical Object Detection
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e. g. pixels.
nnU-Net for Brain Tumor Segmentation
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.
OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images
Segmentation of endoscopic images is an essential processing step for computer and robotics-assisted interventions.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge
The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem.
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.
ModelHub.AI: Dissemination Platform for Deep Learning Models
Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others.
An attempt at beating the 3D U-Net
The U-Net is arguably the most successful segmentation architecture in the medical domain.
Reg R-CNN: Lesion Detection and Grading under Noisy Labels
To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model.
Deep Probabilistic Modeling of Glioma Growth
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
Combined tract segmentation and orientation mapping for bundle-specific tractography
It is based on a learned mapping from the original fiber orientation distribution function (FOD) peaks to tract specific peaks, called tract orientation maps.
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the need for annotations.
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
No New-Net
By incorporating additional measures such as region based training, additional training data, a simple postprocessing technique and a combination of loss functions, we obtain Dice scores of 77. 88, 87. 81 and 80. 62, and Hausdorff Distances (95th percentile) of 2. 90, 6. 03 and 5. 08 for the enhancing tumor, whole tumor and tumor core, respectively on the test data.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015.
Ranked #3 on
Medical Image Segmentation
on Synapse multi-organ CT
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
We propose model-based domain adaptation to overcome input dependencies and avoid re-training of networks at clinical sites by restoring training inputs from altered input channels given during deployment.
Tract orientation mapping for bundle-specific tractography
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce.
A Probabilistic U-Net for Segmentation of Ambiguous Images
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.
TractSeg - Fast and accurate white matter tract segmentation
The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains.
Towards whole-body CT Bone Segmentation
Bone segmentation from CT images is a task that has been worked on for decades.
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
Quantitative analysis of brain tumors is critical for clinical decision making.
Ranked #2 on
Brain Tumor Segmentation
on BRATS-2015
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features
We evaluated our method on the ACDC dataset (4 pathology groups, 1 healthy group) and achieve dice scores of 0. 945 (LVC), 0. 908 (RVC) and 0. 905 (LVM) in a cross-validation over the training set (100 cases) and 0. 950 (LVC), 0. 923 (RVC) and 0. 911 (LVM) on the test set (50 cases).
Direct White Matter Bundle Segmentation using Stacked U-Nets
We present a novel supervised approach for direct tract segmentation that shows major performance gains.
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies.
