no code implementations • 20 Sep 2022 • Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar, Ramona Woitek, Cathal McCague, James D. Brenton, Ozan Öktem, Evis Sala, Leonardo Rundo
On the contrary, we propose a scalable and intuitive framework to calibrate ensembles of deep learning models to produce uncertainty quantification measurements that approximate the classification probability.
no code implementations • 16 Jun 2022 • Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Vinas Torne, Evis Sala, Pietro Lio, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
1 code implementation • 18 Nov 2021 • Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
no code implementations • 31 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.
no code implementations • 31 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.
1 code implementation • 31 Oct 2021 • Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang
However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice.
no code implementations • 16 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.
5 code implementations • 8 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.
no code implementations • 14 Aug 2020 • Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
no code implementations • 24 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.
no code implementations • 15 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.
no code implementations • 14 Jun 2019 • Changhee Han, Leonardo Rundo, Kohei Murao, Zoltán Ádám Milacski, Kazuki Umemoto, Evis Sala, Hideki Nakayama, Shin'ichi Satoh
Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects.
Generative Adversarial Network Unsupervised Anomaly Detection