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Found 15 papers, 0 papers with code
CvS: Classification via Segmentation For Small Datasets
The success of deep learning methods relies heavily on the availability of a large amount of data.
AutoPtosis
Blepharoptosis, or ptosis as it is more commonly referred to, is a condition of the eyelid where the upper eyelid droops.
I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings
However, real-world data is different and translations are yielding varying results.
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives
Even with a simulated false negative rate as high as 50%, applying our loss function to randomly censored data preserves maximum sensitivity at 97% of the baseline with uncensored training data, compared to just 10% for a standard loss function.
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study
A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i. e. exterior boundary map) from a sequence of posed RGB images.
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss
To achieve the best results from data augmentation, our model uses the intersection-over-union (IOU) loss function, a close relative of the Dice loss.
Institutionally Distributed Deep Learning Networks
In such cases, sharing a deep learning model is a more attractive alternative.
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
Breast cancer has the highest incidence and second highest mortality rate for women in the US.
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI
We present a vision-only model for gaming AI which uses a late integration deep convolutional network architecture trained in a purely supervised imitation learning context.
3-D Convolutional Neural Networks for Glioblastoma Segmentation
In this paper, we propose a framework of 3-D fully CNN models for Glioblastoma segmentation from multi-modality MRI data.
