Search Results for author:
Found 31 papers, 16 papers with code
Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis
In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores.
Approaching Peak Ground Truth
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such.
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex.
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
blob loss: instance imbalance aware loss functions for semantic segmentation
Blob loss is designed for semantic segmentation problems in which the instances are the connected components within a class.
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Relationformer: A Unified Framework for Image-to-Graph Generation
We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning.
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge
Importantly, we explore the inclusion of a transformer in the bottleneck of the U-Net architecture.
FedCostWAvg: A new averaging for better Federated Learning
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community.
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.
Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations
In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations
Radiomic representations can quantify properties of regions of interest in medical image data.
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
With this growth, however, come new challenges for the community.
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another medical imaging perspective
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee
The existing implicit schemes are usually iterative and employ a general-purpose solver which may be sub-optimal for a specific class of PDEs.
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.
Neural parameters estimation for brain tumor growth modeling
In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
