Leonardo Rundo | DeepAI

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∙ 09/20/2022

Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation

Uncertainty quantification in automated image analysis is highly desired...

0 Thomas Buddenkotte, et al. ∙

∙ 10/31/2021

Focal Attention Networks: optimising attention for biomedical image segmentation

In recent years, there has been increasing interest to incorporate atten...

0 Michael Yeung, et al. ∙

∙ 10/31/2021

Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation

Manual segmentation is used as the gold-standard for evaluating neural n...

0 Michael Yeung, et al. ∙

∙ 10/31/2021

Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation

The Dice similarity coefficient (DSC) is both a widely used metric and l...

6 Michael Yeung, et al. ∙

∙ 06/04/2021

Computer-Assisted Analysis of Biomedical Images

Nowadays, the amount of heterogeneous biomedical data is increasing more...

0 Leonardo Rundo, et al. ∙

∙ 05/16/2021

Advances in Artificial Intelligence to Reduce Polyp Miss Rates during Colonoscopy

BACKGROUND AND CONTEXT: Artificial intelligence has the potential to aid...

20 Michael Yeung, et al. ∙

∙ 02/08/2021

A Mixed Focal Loss Function for Handling Class Imbalanced Medical Image Segmentation

Automatic segmentation methods are an important advancement in medical i...

22 Michael Yeung, et al. ∙

∙ 07/24/2020

MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction

Unsupervised learning can discover various unseen diseases, relying on l...

7 Changhee Han, et al. ∙

∙ 01/12/2020

Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems

This work aims to identify/bridge the gap between Artificial Intelligenc...

0 Changhee Han, et al. ∙

∙ 06/14/2019

GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis

Leveraging large-scale healthy datasets, unsupervised learning can disco...

4 Changhee Han, et al. ∙

∙ 06/12/2019

Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection

Accurate computer-assisted diagnosis, relying on large-scale annotated p...

6 Changhee Han, et al. ∙

∙ 05/31/2019

Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

Convolutional Neural Networks (CNNs) can achieve excellent computer-assi...

0 Changhee Han, et al. ∙

∙ 04/17/2019

USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Prostate cancer is the most common malignant tumors in men but prostate ...

0 Leonardo Rundo, et al. ∙

∙ 03/29/2019

CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study

Prostate cancer is the most common cancer among US men. However, prostat...

0 Leonardo Rundo, et al. ∙

∙ 03/29/2019

Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection

Due to the lack of available annotated medical images, accurate computer...

22 Changhee Han, et al. ∙

∙ 02/26/2019

Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR images

Accurate computer-assisted diagnosis can alleviate the risk of overlooki...

0 Changhee Han, et al. ∙

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