Search Results for author: Leonardo Rundo

Found 16 papers, 2 papers with code

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

1 code implementation31 Oct 2021 Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang

However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians.

Semantic Segmentation

Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation

no code implementations31 Oct 2021 Michael Yeung, Guang Yang, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo

Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks.

Semantic Segmentation

Focal Attention Networks: optimising attention for biomedical image segmentation

no code implementations31 Oct 2021 Michael Yeung, Leonardo Rundo, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang

In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation.

Semantic Segmentation

Computer-Assisted Analysis of Biomedical Images

no code implementations4 Jun 2021 Leonardo Rundo

Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies.

Decision Making

Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy

no code implementations16 May 2021 Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo

When evaluated on a combination of five public polyp datasets, our model similarly achieves state-of-the-art results with a mean DSC of 0. 878 and mean IoU of 0. 809, a 14% and 15% improvement over the previous state-of-the-art results of 0. 768 and 0. 702, respectively.

Semantic Segmentation

Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

4 code implementations8 Feb 2021 Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo

We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust to class imbalance and consistently outperforms the other loss functions.

Medical Image Segmentation

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

no code implementations24 Jul 2020 Changhee Han, Leonardo Rundo, Kohei Murao, Tomoyuki Noguchi, Yuki Shimahara, Zoltan Adam Milacski, Saori Koshino, Evis Sala, Hideki Nakayama, Shinichi Satoh

Therefore, we propose unsupervised Medical Anomaly Detection Generative Adversarial Network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 L1 loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average L2 loss per scan discriminates them, comparing the ground truth/reconstructed slices.

MRI Reconstruction Unsupervised Anomaly Detection

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning

no code implementations15 May 2020 Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek

This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.

Image Registration

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

no code implementations12 Jan 2020 Changhee Han, Leonardo Rundo, Kohei Murao, Takafumi Nemoto, Hideki Nakayama

Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training.

Image Augmentation Image Generation

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

no code implementations31 May 2019 Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi

In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e. g., random noise samples to diverse pathological images) or image-to-image GANs (e. g., a benign image to a malignant one).

General Classification Image Augmentation +2

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

no code implementations17 Apr 2019 Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga

The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.

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

no code implementations29 Mar 2019 Changhee Han, Leonardo Rundo, Ryosuke Araki, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi

Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement.

Data Augmentation

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