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Found 18 papers, 7 papers with code
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation
Data augmentation plays a crucial role in addressing the challenge of limited expert-annotated datasets in deep learning applications for retinal Optical Coherence Tomography (OCT) scans.
RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification
The framework consists of a fully convolutional neural network that recursively refines semantic segmentation maps, correcting manifest classification errors and thus improving topological consistency.
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Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks
The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation due to its impressive capabilities and prompt-based interface.
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
Moreover, the proposed SSL method allows further improvement of this performance by up to 23%, and we show that the SSL is beneficial regardless of the network architecture.
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world.
SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina.
Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening
The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly reduce the impact of this disease.
HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year.
LNDb: A Lung Nodule Database on Computed Tomography
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients.
DR$\vert$GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
We show that high QWK values occur for images with low prediction uncertainty, thus indicating that this uncertainty is a valid measure of the predictions' quality.
Did you miss it? Automatic lung nodule detection combined with gaze information improves radiologists' screening performance
Likewise, combining the findings of radiologist with the detection algorithm only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.
UOLO - automatic object detection and segmentation in biomedical images
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images.
BACH: Grand Challenge on Breast Cancer Histology Images
From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images.
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis
Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants.
