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Found 15 papers, 7 papers with code
Towards Adversarial Retinal Image Synthesis
These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image.
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.
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.
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.
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.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
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.
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.
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.
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world.
Enhancement of Retinal Fundus Images via Pixel Color Amplification
We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images.
Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector
Cancer is one of the leading causes of death in the developed world.
HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection
HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
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.
