Search Results for author: Pietro Lio

Found 54 papers, 22 papers with code

Masked Attention is All You Need for Graphs

no code implementations16 Feb 2024 David Buterez, Jon Paul Janet, Dino Oglic, Pietro Lio

Graph neural networks (GNNs) and variations of the message passing algorithm are the predominant means for learning on graphs, largely due to their flexibility, speed, and satisfactory performance.

Transfer Learning

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

1 code implementation12 Dec 2023 Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael Bronstein

In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations.

Protein Structure Prediction Specificity

Incorporating LLM Priors into Tabular Learners

no code implementations20 Nov 2023 Max Zhu, Siniša Stanivuk, Andrija Petrovic, Mladen Nikolic, Pietro Lio

We present a method to integrate Large Language Models (LLMs) and traditional tabular data classification techniques, addressing LLMs challenges like data serialization sensitivity and biases.

regression

Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation

no code implementations9 Nov 2023 Jialin Chen, Yuelin Wang, Cristian Bodnar, Rex Ying, Pietro Lio, Yu Guang Wang

However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue.

Node Classification

Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design

no code implementations11 Oct 2023 Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernardez, Olga Zaghen, Simone Scardapane, Pietro Lio

Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to overshadowing specific characteristics of hypergraphs.

Benchmarking Representation Learning

Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?

1 code implementation14 Aug 2023 Charles Harris, Kieran Didi, Arian R. Jamasb, Chaitanya K. Joshi, Simon V. Mathis, Pietro Lio, Tom Blundell

Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years.

Benchmarking

DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping

1 code implementation14 Aug 2023 Jos Torge, Charles Harris, Simon V. Mathis, Pietro Lio

Scaffold hopping is a drug discovery strategy to generate new chemical entities by modifying the core structure, the \emph{scaffold}, of a known active compound.

Drug Discovery

Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models

no code implementations8 Jun 2023 Aleksa Bisercic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Pietro Lio, Andrija Petrovic

Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text.

text-classification Text Classification

HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

no code implementations6 Mar 2023 Md Shofiqul Islam, Khondokar Fida Hasan, Sunjida Sultana, Shahadat Uddin, Pietro Lio, Julian M. W. Quinn, Mohammad Ali Moni

The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99. 60\%, F1 score of 98. 21\%, a precision of 97. 66\%, and recall of 99. 60\% using MIT-BIH generated ECG.

Denoising Generative Adversarial Network +1

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

1 code implementation24 Feb 2023 Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lio, Mihaela van der Schaar

SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.

Fairness Survival Analysis

A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning

1 code implementation22 Feb 2023 Jin Zhu, Guang Yang, Pietro Lio

On the other hand, the segmentation-based perceptual loss increases $+0. 14$ dB PSNR on average for SOTA methods, including CNNs and vision transformers.

Image Segmentation Image Super-Resolution +2

A Federated Learning Benchmark for Drug-Target Interaction

1 code implementation15 Feb 2023 Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lio

Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs.

Federated Learning Privacy Preserving

GCI: A (G)raph (C)oncept (I)nterpretation Framework

1 code implementation9 Feb 2023 Dmitry Kazhdan, Botty Dimanov, Lucie Charlotte Magister, Pietro Barbiero, Mateja Jamnik, Pietro Lio

Explainable AI (XAI) underwent a recent surge in research on concept extraction, focusing on extracting human-interpretable concepts from Deep Neural Networks.

Explainable Artificial Intelligence (XAI) Molecular Property Prediction +1

DBGDGM: Dynamic Brain Graph Deep Generative Model

no code implementations26 Jan 2023 Alexander Campbell, Simeon Spasov, Nicola Toschi, Pietro Lio

In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.

Dynamic Link Prediction Graph Classification +1

GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data

no code implementations11 Nov 2022 Andrei Margeloiu, Nikola Simidjievski, Pietro Lio, Mateja Jamnik

We create a graph between samples for each data dimension, and utilise Graph Neural Networks (GNNs) for extracting this implicit structure, and for conditioning the parameters of the first layer of an underlying predictor network.

Vocal Bursts Intensity Prediction

Extending Logic Explained Networks to Text Classification

1 code implementation4 Nov 2022 Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Davide Buffelli, Pietro Lio

Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions.

text-classification Text Classification

Composite Feature Selection using Deep Ensembles

2 code implementations1 Nov 2022 Fergus Imrie, Alexander Norcliffe, Pietro Lio, Mihaela van der Schaar

To do so, we define predictive groups in terms of linear and non-linear interactions between features.

feature selection

DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning

2 code implementations27 Sep 2022 Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti, Nicola Toschi, Pietro Lio

Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data.

Graph structure learning

Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

no code implementations1 Apr 2022 Jiahao Huang, Yingying Fang, Yang Nan, Huanjun Wu, Yinzhe Wu, Zhifan Gao, Yang Li, Zidong Wang, Pietro Lio, Daniel Rueckert, Yonina C. Eldar, Guang Yang

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.

Anatomy Explainable Models +3

Spatio-temporal Vision Transformer for Super-resolution Microscopy

1 code implementation28 Feb 2022 Charles N. Christensen, Meng Lu, Edward N. Ward, Pietro Lio, Clemens F. Kaminski

Structured illumination microscopy (SIM) is an optical super-resolution technique that enables live-cell imaging beyond the diffraction limit.

Motion Estimation Optical Flow Estimation +1

Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI

no code implementations11 Nov 2021 Junwei Yang, Xiao-Xin Li, Feihong Liu, Dong Nie, Pietro Lio, Haikun Qi, Dinggang Shen

Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modalities.

MRI Reconstruction

Adaptive Filters for Low-Latency and Memory-Efficient Graph Neural Networks

no code implementations ICLR 2022 Shyam A. Tailor, Felix Opolka, Pietro Lio, Nicholas Donald Lane

Scaling and deploying graph neural networks (GNNs) remains difficult due to their high memory consumption and inference latency.

On Second Order Behaviour in Augmented Neural ODEs: A Short Summary

no code implementations NeurIPS Workshop DLDE 2021 Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lio

In Norcliffe et al.[13], we discussed and systematically analysed how Neural ODEs (NODEs) can learn higher-order order dynamics.

Neural ODE Processes: A Short Summary

1 code implementation NeurIPS Workshop DLDE 2021 Alexander Luke Ian Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Lio

To this end, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs.

Time Series Time Series Analysis

Modular Neural Ordinary Differential Equations

no code implementations15 Sep 2021 Max Zhu, Pietro Lio, Jacob Moss

The laws of physics have been written in the language of dif-ferential equations for centuries.

$\alpha$-VAEs : Optimising variational inference by learning data-dependent divergence skew

no code implementations ICML Workshop INNF 2021 Jacob Deasy, Tom Andrew McIver, Nikola Simidjievski, Pietro Lio

The {\em skew-geometric Jensen-Shannon divergence} $\left(\textrm{JS}^{\textrm{G}_{\alpha}}\right)$ allows for an intuitive interpolation between forward and reverse Kullback-Leibler (KL) divergence based on the skew parameter $\alpha$.

Denoising Variational Inference

How Framelets Enhance Graph Neural Networks

1 code implementation13 Feb 2021 Xuebin Zheng, Bingxin Zhou, Junbin Gao, Yu Guang Wang, Pietro Lio, Ming Li, Guido Montufar

The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks.

Denoising

Learned Low Precision Graph Neural Networks

no code implementations19 Sep 2020 Yiren Zhao, Duo Wang, Daniel Bates, Robert Mullins, Mateja Jamnik, Pietro Lio

LPGNAS learns the optimal architecture coupled with the best quantisation strategy for different components in the GNN automatically using back-propagation in a single search round.

GRADE: Graph Dynamic Embedding

no code implementations16 Jul 2020 Simeon Spasov, Alessandro Di Stefano, Pietro Lio, Jian Tang

At each time step link generation is performed by first assigning node membership from a distribution over the communities, and then sampling a neighbor from a distribution over the nodes for the assigned community.

Community Detection Dynamic Community Detection +3

Path Integral Based Convolution and Pooling for Graph Neural Networks

1 code implementation NeurIPS 2020 Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Lio

Borrowing ideas from physics, we propose a path integral based graph neural networks (PAN) for classification and regression tasks on graphs.

Graph Classification Graph Regression +1

Abstract Diagrammatic Reasoning with Multiplex Graph Networks

no code implementations ICLR 2020 Duo Wang, Mateja Jamnik, Pietro Lio

We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM).

Visual Reasoning

Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds

no code implementations15 Jun 2020 Duo Wang, Mateja Jamnik, Pietro Lio

We show that neural nets with this inductive bias achieve considerably better o. o. d generalisation performance for a range of relational reasoning tasks.

Inductive Bias Relational Reasoning +1

ML-SIM: A deep neural network for reconstruction of structured illumination microscopy images

1 code implementation24 Mar 2020 Charles N. Christensen, Edward N. Ward, Pietro Lio, Clemens F. Kaminski

Structured illumination microscopy (SIM) has become an important technique for optical super-resolution imaging because it allows a doubling of image resolution at speeds compatible for live-cell imaging.

Super-Resolution

Probabilistic Dual Network Architecture Search on Graphs

no code implementations21 Mar 2020 Yiren Zhao, Duo Wang, Xitong Gao, Robert Mullins, Pietro Lio, Mateja Jamnik

We present the first differentiable Network Architecture Search (NAS) for Graph Neural Networks (GNNs).

Learning distributed representations of graphs with Geo2DR

1 code implementation12 Mar 2020 Paul Scherer, Pietro Lio

We present Geo2DR (Geometric to Distributed Representations), a GPU ready Python library for unsupervised learning on graph-structured data using discrete substructure patterns and neural language models.

Graph Classification Language Modelling

Decoupling feature propagation from the design of graph auto-encoders

no code implementations18 Oct 2019 Paul Scherer, Helena Andres-Terre, Pietro Lio, Mateja Jamnik

We present two instances, L-GAE and L-VGAE, of the variational graph auto-encoding family (VGAE) based on separating feature propagation operations from graph convolution layers typically found in graph learning methods to a single linear matrix computation made prior to input in standard auto-encoder architectures.

Graph Learning Graph Representation Learning +1

How can we generalise learning distributed representations of graphs?

no code implementations25 Sep 2019 Paul M Scherer, Pietro Lio

We propose a general framework to construct unsupervised models capable of learning distributed representations of discrete structures such as graphs based on R-Convolution kernels and distributed semantics research.

Binary Classification Graph Classification +1

Empowering Graph Representation Learning with Paired Training and Graph Co-Attention

no code implementations25 Sep 2019 Andreea Deac, Yu-Hsiang Huang, Petar Velickovic, Pietro Lio, Jian Tang

Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.

Graph Classification Graph Regression +2

Dynamic Neural Network Channel Execution for Efficient Training

no code implementations15 May 2019 Simeon E. Spasov, Pietro Lio

Existing methods for reducing the computational burden of neural networks at run-time, such as parameter pruning or dynamic computational path selection, focus solely on improving computational efficiency during inference.

Computational Efficiency Image Classification

Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat

no code implementations14 Mar 2019 Duo Wang, Mateja Jamnik, Pietro Lio

In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a Recurrent Auto-Encoder with structured latent distributions containing discrete categorical distributions, continuous attribute distributions, and factorised spatial attention.

Attribute

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Lesion Focused Super-Resolution

2 code implementations15 Oct 2018 Jin Zhu, Guang Yang, Pietro Lio

Super-resolution (SR) for image enhancement has great importance in medical image applications.

Brain Tumor Segmentation Image Enhancement +3

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