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Found 27 papers, 13 papers with code
Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach
Accurate and automatic segmentation of intervertebral discs from medical images is a critical task for the assessment of spine-related diseases such as osteoporosis, vertebral fractures, and intervertebral disc herniation.
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
Gliomas are one of the most prevalent types of primary brain tumours, accounting for more than 30\% of all cases and they develop from the glial stem or progenitor cells.
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging.
Contextual Attention Network: Transformer Meets U-Net
Then, they emphasize the informative regions while taking into account the long-range contextual dependency derived by the Transformer module.
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning
In digital pathology, many image analysis tasks are challenged by the need for large and time-consuming manual data annotations to cope with various sources of variability in the image domain.
Multiscale Softmax Cross Entropy for Fovea Localization on Color Fundus Photography
Fovea localization is one of the most popular tasks in ophthalmic medical image analysis, where the coordinates of the center point of the macula lutea, i. e. fovea centralis, should be calculated based on color fundus images.
The information content of brain states is explained by structural constraints on state energetics
These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence.
High-throughput Phenotyping of Nematode Cysts
The beet cyst nematode (BCN) Heterodera schachtii is a plant pest responsible for crop loss on a global scale.
Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations
In our work, we propose a new method to overcome catastrophic forgetting and thus successfully fine-tune pre-trained representations for AD in the transfer learning setting.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation
A distance regression module is incorporated into our architecture to generate seeds for fast clustering.
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
We follow an alternative approach and demonstrate that deep feature representations learned by discriminative models on large natural image datasets are well suited to describe normality and detect even subtle anomalies in a transfer learning setting.
Ranked #19 on
Anomaly Detection
on MVTec AD
(using extra training data)
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018
Fueled by recent advances in machine learning, there has been tremendous progress in the field of semantic segmentation for the medical image computing community.
An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications.
MixNet: Multi-modality Mix Network for Brain Segmentation
Automated brain structure segmentation is important to many clinical quantitative analysis and diagnoses.
Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd.
Radiomic Feature Stability Analysis based on Probabilistic Segmentations
From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations.
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Achieving an AUROC of 0. 89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist.
Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters
We present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
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.
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features.
Spherical Harmonic Residual Network for Diffusion Signal Harmonization
For this purpose, a training database is required, which consist of the same subjects, scanned on different scanners.
DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging
DELIMIT is a framework extension for deep learning in diffusion imaging, which extends the basic framework PyTorch towards spherical signals.
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios.
Context-based Normalization of Histological Stains using Deep Convolutional Features
While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available.
CNN Cascades for Segmenting Whole Slide Images of the Kidney
Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems.
Recycle deep features for better object detection
Aiming at improving the performance of existing detection algorithms developed for different applications, we propose a region regression-based multi-stage class-agnostic detection pipeline, whereby the existing algorithms are employed for providing the initial detection proposals.
