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Found 24 papers, 16 papers with code
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets.
Scalable multimodal convolutional networks for brain tumour segmentation
Brain tumour segmentation plays a key role in computer-assisted surgery.
Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks
3) We show that the joint use of holistic CNNs and generalised Wasserstein Dice scores achieves segmentations that are more semantically meaningful for brain tumour segmentation.
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images.
NiftyNet: a deep-learning platform for medical imaging
NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
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.
SGD with Hardness Weighted Sampling for Distributionally Robust Deep Learning
Similar to a hard example mining strategy in essence and in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning.
Distributionally Robust Deep Learning using Hardness Weighted Sampling
In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM).
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans.
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge
We stuck to a generic and state-of-the-art 3D U-Net architecture and experimented with a non-standard per-sample loss function, the generalized Wasserstein Dice loss, a non-standard population loss function, corresponding to distributionally robust optimization, and a non-standard optimizer, Ranger.
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding.
Image Compositing for Segmentation of Surgical Tools without Manual Annotations
Background data is captured by collecting videos that do not contain instruments.
MONAIfbs: MONAI-based fetal brain MRI deep learning segmentation
In this work, we aim at improving the fetal brain segmentation for SRR in Spina Bifida.
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images.
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
The performance of deep neural networks typically increases with the number of training images.
Partial supervision for the FeTA challenge 2021
Label-set loss functions allow to train deep neural networks with partially segmented images, i. e. segmentations in which some classes may be grouped into super-classes.
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.
ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation
Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes.
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
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.
blob loss: instance imbalance aware loss functions for semantic segmentation
\emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Trustworthy Deep Learning for Medical Image Segmentation
One major limitation of deep learning-based segmentation methods is their lack of robustness to variability in the image acquisition protocol and in the imaged anatomy that were not represented or were underrepresented in the training dataset.
