• Article |

    A fundamental problem in network science is how to find an optimal set of key players whose activation or removal significantly impacts network functionality. The authors propose a deep reinforcement learning framework that can be trained on small networks to understand the organizing principles of complex networked systems.

    • Changjun Fan
    • , Li Zeng
    • , Yizhou Sun
    •  & Yang-Yu Liu
  • Article |

    Predicting overall survival for patients with confirmed non-small-cell lung cancer is an important issue in clinical practice. The authors developed and validated in four independent patient cohorts a shallow convolutional neural network that can predict the outcomes of individuals using pre-treatment CT images. The authors further show that the survival model can be used, via transfer learning, for classifying benign versus malignant nodules.

    • Pritam Mukherjee
    • , Mu Zhou
    • , Edward Lee
    • , Anne Schicht
    • , Yoganand Balagurunathan
    • , Sandy Napel
    • , Robert Gillies
    • , Simon Wong
    • , Alexander Thieme
    • , Ann Leung
    •  & Olivier Gevaert
  • Article |

    While computerization and digitization of medicine have advanced substantially, management tools in healthcare have not yet benefited much from these developments due to the extreme complexity and variability of healthcare operations. The ability of machine learning algorithms to build strong models from a large number of weakly predictive features, and to identify key factors in complex feature sets, is tested in operational problems involving hospital datasets on workflow and patient waiting time.

    • Oleg S. Pianykh
    • , Steven Guitron
    • , Darren Parke
    • , Chengzhao Zhang
    • , Pari Pandharipande
    • , James Brink
    •  & Daniel Rosenthal
  • Article |

    Early and accurate clinical assessment of disease severity in COVID-19 patients is essential for planning the allocation of scarce hospital resources. An explainable machine learning tool trained on blood sample data from 485 patients from Wuhan selected three biomarkers for predicting mortality of individual patients with high accuracy.

    • Li Yan
    • , Hai-Tao Zhang
    • , Jorge Goncalves
    • , Yang Xiao
    • , Maolin Wang
    • , Yuqi Guo
    • , Chuan Sun
    • , Xiuchuan Tang
    • , Liang Jing
    • , Mingyang Zhang
    • , Xiang Huang
    • , Ying Xiao
    • , Haosen Cao
    • , Yanyan Chen
    • , Tongxin Ren
    • , Fang Wang
    • , Yaru Xiao
    • , Sufang Huang
    • , Xi Tan
    • , Niannian Huang
    • , Bo Jiao
    • , Cheng Cheng
    • , Yong Zhang
    • , Ailin Luo
    • , Laurent Mombaerts
    • , Junyang Jin
    • , Zhiguo Cao
    • , Shusheng Li
    • , Hui Xu
    •  & Ye Yuan
  • Article |

    Current neural networks attempt to learn spatial and temporal information as a whole, limiting their ability to process complex video data. Pang et al. improve performance by introducing a network structure which learns to implicitly decouple complex spatial and temporal concepts.

    • Bo Pang
    • , Kaiwen Zha
    • , Hanwen Cao
    • , Jiajun Tang
    • , Minghui Yu
    •  & Cewu Lu
  • Article |

    The deep convolutional recurrent neural network ‘PredNet’ can be trained to predict future video frames in a self-supervised manner. A surprising result is that it captures a wide array of phenomena observed in natural neuronal systems, ranging from low-level visual cortical neuron response properties to high-level perceptual illusions, hinting at potential similarities between recurrent predictive neural network models and computations in the brain.

    • William Lotter
    • , Gabriel Kreiman
    •  & David Cox
  • Article |

    4D MRI scans can reconstruct cardiovascular flow, although they typically take many minutes, hindering real-time assessment. Vishnevskiy et al. develop a deep variational network to permit high-fidelity image reconstruction in a matter of seconds, allowing integration of 4D flow MRI into clinical workflows.

    • Valery Vishnevskiy
    • , Jonas Walheim
    •  & Sebastian Kozerke
  • Article |

    A lack of accurate and efficient variant calling methods has held back single-molecule sequencing technologies from clinical applications. The authors present a deep-learning method for fast and accurate germline small variant calling, using single-molecule sequencing data.

    • Ruibang Luo
    • , Chak-Lim Wong
    • , Yat-Sing Wong
    • , Chi-Ian Tang
    • , Chi-Man Liu
    • , Chi-Ming Leung
    •  & Tak-Wah Lam
  • Article |

    Finding the best ratio of ingredients for polymerization reactions can be time consuming and wasteful. An automated microreactor process with integrated machine learning analysis initiates reactions, measures the resulting yield and cleans itself without human intervention. It can test concentrations of reagents systematically to find the combination with the highest production, while producing a low amount of waste.

    • Benjamin A. Rizkin
    • , Albert S. Shkolnik
    • , Neil J. Ferraro
    •  & Ryan L. Hartman
  • Article |

    Integrating knowledge about the circuit-level organization of the brain into neuromorphic artificial systems is a challenging research problem. The authors present a neural algorithm for the learning of odourant signals and their robust identification under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system.

    • Nabil Imam
    •  & Thomas A. Cleland
  • Article |

    With the aid of deep learning, the space of chemical molecules, such as candidates for drugs, can be constrained to find new bioactive molecules. A new open source tool can generate libraries of novel molecules with user defined properties.

    • Michael Moret
    • , Lukas Friedrich
    • , Francesca Grisoni
    • , Daniel Merk
    •  & Gisbert Schneider
  • Article |

    Getting safe and fast access to blood vessels is vital to many methods of treatment and diagnosis in medicine. Robot-assisted or even fully autonomous methods can potentially do the task more reliably than humans, especially when veins are hard to detect. In this work, a method is tested that uses deep learning to find blood vessels and track the movement of a patient’s arm.

    • Alvin I. Chen
    • , Max L. Balter
    • , Timothy J. Maguire
    •  & Martin L. Yarmush
  • Article |

    When predicting the interaction of proteins with potential drugs, the protein can be encoded as its one-dimensional sequence or a three-dimensional structure, which can capture more relevant features of the protein, but also makes the task to predict the interactions harder. A new method predicts these interactions using a two-dimensional distance matrix representation of a protein, which can be processed like a two-dimensional image, striking a balance between the data being simple to process and rich in relevant structures.

    • Shuangjia Zheng
    • , Yongjian Li
    • , Sheng Chen
    • , Jun Xu
    •  & Yuedong Yang
  • Article |

    Age-related macular degeneration is a serious eye disease which should be detected as early as possible. Using both fundus images and genetic information, a deep neural network is able to detect the severity of the disease and predict its progression seven years into the future.

    • Qi Yan
    • , Daniel E. Weeks
    • , Hongyi Xin
    • , Anand Swaroop
    • , Emily Y. Chew
    • , Heng Huang
    • , Ying Ding
    •  & Wei Chen
  • Article |

    Counting different types of circulating tumour cells can give valuable information on the severity of the disease and on whether treatments are effective for a specific patient. In this work, the authors show that their method based on autoencoders can identify and count cells more accurately and faster than human experts.

    • Leonie L. Zeune
    • , Yoeri E. Boink
    • , Guus van Dalum
    • , Afroditi Nanou
    • , Sanne de Wit
    • , Kiki C. Andree
    • , Joost F. Swennenhuis
    • , Stephan A. van Gils
    • , Leon W.M.M. Terstappen
    •  & Christoph Brune
  • Article |

    Neural networks are often implemented with reduced precision in order to meet the tight energy and memory budget required by edge computing devices. Chakraborty et al. develop a technique for assessing which layers can be quantized, and by how much, without sacrificing too much on performance.

    • Indranil Chakraborty
    • , Deboleena Roy
    • , Isha Garg
    • , Aayush Ankit
    •  & Kaushik Roy
  • Article |

    By assembling conceptual systems from real-word datasets of text, images and audio, Roads and Love propose that objects embedded within a conceptual system have a unique signature that allows for conceptual systems to be aligned in an unsupervised fashion.

    • Brett D. Roads
    •  & Bradley C. Love
  • Article |

    Tree-based machine learning models are widely used in domains such as healthcare, finance and public services. The authors present an explanation method for trees that enables the computation of optimal local explanations for individual predictions, and demonstrate their method on three medical datasets.

    • Scott M. Lundberg
    • , Gabriel Erion
    • , Hugh Chen
    • , Alex DeGrave
    • , Jordan M. Prutkin
    • , Bala Nair
    • , Ronit Katz
    • , Jonathan Himmelfarb
    • , Nisha Bansal
    •  & Su-In Lee
  • Article |

    Predicting the structure of proteins from amino acid sequences is a hard problem. Convolutional neural networks can learn to predict a map of distances between amino acid residues that can be turned into a three-dimensional structure. With a combination of approaches, including an evolutionary technique to find the best neural network architecture and a tool to find the atom coordinates in the folded structure, a pipeline for rapid prediction of three-dimensional protein structures is demonstrated.

    • Wenzhi Mao
    • , Wenze Ding
    • , Yaoguang Xing
    •  & Haipeng Gong
  • Article |

    Number processing is linked to bodily systems, especially finger movements. The authors apply convolutional neural network models in the context of cognitive developmental robotics. They show that proprioceptive information in the child-like robot iCub improves accuracy and recognition of spoken digits.

    • Alessandro Di Nuovo
    •  & James L. McClelland
  • Article |

    Haptic interfaces are important for the development of immersive human–machine interactions. To create a compact design with rich touch-sensitive functions, a robotic device called Foldaway, which folds flat, has been designed that can render three-degrees-of-freedom force feedback.

    • Stefano Mintchev
    • , Marco Salerno
    • , Alexandre Cherpillod
    • , Simone Scaduto
    •  & Jamie Paik
  • Article |

    Identifying abnormalities in medical images across different viewing angles and body parts is a time-consuming task. Deep learning techniques hold great promise for supporting radiologists and improving patient triage decisions. A new study tests the viability of such approaches in resource-limited settings, exploring the effect of pretraining, dataset size and choice of deep learning model in the task of abnormality detection in lower-limb radiographs.

    • Maya Varma
    • , Mandy Lu
    • , Rachel Gardner
    • , Jared Dunnmon
    • , Nishith Khandwala
    • , Pranav Rajpurkar
    • , Jin Long
    • , Christopher Beaulieu
    • , Katie Shpanskaya
    • , Li Fei-Fei
    • , Matthew P. Lungren
    •  & Bhavik N. Patel
  • Article |

    Drug combinations are often an effective means of managing complex diseases, but understanding the synergies of drug combinations requires extensive resources. The authors developed an efficient machine learning model that requires only a limited set of pairwise dose–response measurements for the accurate prediction of synergistic and antagonistic drug combinations.

    • Aleksandr Ianevski
    • , Anil K. Giri
    • , Prson Gautam
    • , Alexander Kononov
    • , Swapnil Potdar
    • , Jani Saarela
    • , Krister Wennerberg
    •  & Tero Aittokallio
  • Article |

    To better extract meaning from natural language, some less informative words can be removed before a model is trained, which is usually done by using manually curated lists of stopwords. A new information theoretic approach can identify uninformative words automatically and more accurately.

    • Martin Gerlach
    • , Hanyu Shi
    •  & Luís A. Nunes Amaral
  • Article |

    Algorithms and bots are capable of performing some behaviours at human or super-human levels. Humans, however, tend to trust algorithms less than they trust other humans. The authors find that bots do better than humans at inducing cooperation in certain human–machine interactions, but only if the bots do not disclose their true nature as artificial.

    • Fatimah Ishowo-Oloko
    • , Jean-François Bonnefon
    • , Zakariyah Soroye
    • , Jacob Crandall
    • , Iyad Rahwan
    •  & Talal Rahwan
  • Article |

    Human face recognition is robust to changes in viewpoint, illumination, facial expression and appearance. The authors investigated face recognition in deep convolutional neural networks by manipulating the strength of identity information in a face by caricaturing. They found that networks create a highly organized face similarity structure in which identities and images coexist.

    • Matthew Q. Hill
    • , Connor J. Parde
    • , Carlos D. Castillo
    • , Y. Ivette Colón
    • , Rajeev Ranjan
    • , Jun-Cheng Chen
    • , Volker Blanz
    •  & Alice J. O’Toole
  • Article |

    Photonic computing devices have been proposed as a high-speed and energy-efficient approach to implementing neural networks. Using off-the-shelf components, Antonik et al. demonstrate a reservoir computer that recognizes different forms of human action from video streams using photonic neural networks.

    • Piotr Antonik
    • , Nicolas Marsal
    • , Daniel Brunner
    •  & Damien Rontani
  • Article |

    Deep learning is currently transforming digital pathology, helping to make more reliable and faster clinical diagnoses. A promising application is in the recognition of malignant white blood cells—an essential step for detecting acute myeloid leukaemia that is challenging even for trained human examiners. An annotated image dataset of over 18,000 white blood cells is compiled and used to train a convolutional neural network for leukocyte classification. The network classifies the most important cell types with high accuracy and can answer clinically relevant binary questions with human-level performance.

    • Christian Matek
    • , Simone Schwarz
    • , Karsten Spiekermann
    •  & Carsten Marr
  • Article |

    To keep radiation therapy from damaging healthy tissue, expert radiologists have to segment CT scans into individual organs. A new deep learning-based method for delineating organs in the area of head and neck performs faster and more accurately than human experts.

    • Hao Tang
    • , Xuming Chen
    • , Yang Liu
    • , Zhipeng Lu
    • , Junhua You
    • , Mingzhou Yang
    • , Shengyu Yao
    • , Guoqi Zhao
    • , Yi Xu
    • , Tingfeng Chen
    • , Yong Liu
    •  & Xiaohui Xie
  • Article |

    Neural network force fields promise to bypass the computationally expensive quantum mechanical calculations typically required to investigate complex materials, such as lithium-ion batteries. Mailoa et al. accelerate these approaches with an architecture that exploits both rotation-invariant and -covariant features separately.

    • Jonathan P. Mailoa
    • , Mordechai Kornbluth
    • , Simon Batzner
    • , Georgy Samsonidze
    • , Stephen T. Lam
    • , Jonathan Vandermause
    • , Chris Ablitt
    • , Nicola Molinari
    •  & Boris Kozinsky
  • Article |

    Optoacoustic imaging can achieve high spatial and temporal resolution but image quality is often compromised by suboptimal data acquisition. A new method employing deep learning to recover high-quality images from sparse or limited-view optoacoustic scans has been developed and demonstrated for whole-body mouse imaging in vivo.

    • Neda Davoudi
    • , Xosé Luís Deán-Ben
    •  & Daniel Razansky
  • Article |

    Labelling training data to train machine learning models is very time intense. A new method shows that content transformation can be effectively learned from generated data, avoiding the need for any manual labelling in segmentation and classification tasks.

    • Stephan J. Ihle
    • , Andreas M. Reichmuth
    • , Sophie Girardin
    • , Hana Han
    • , Flurin Stauffer
    • , Anne Bonnin
    • , Marco Stampanoni
    • , Karthik Pattisapu
    • , János Vörös
    •  & Csaba Forró
  • Article |

    Brain–machine interfaces using steady-state visually evoked potentials (SSVEPs) show promise in therapeutic applications. With a combination of innovations in flexible and soft electronics and in deep learning approaches to classify potentials from two channels and from any subject, a compact, wireless and universal SSVEP interface is designed. Subjects can operate a wheelchair in real time with eye movements while wearing the new brain–machine interface.

    • Musa Mahmood
    • , Deogratias Mzurikwao
    • , Yun-Soung Kim
    • , Yongkuk Lee
    • , Saswat Mishra
    • , Robert Herbert
    • , Audrey Duarte
    • , Chee Siang Ang
    •  & Woon-Hong Yeo
  • Article |

    A combination of engineering advances shows promise for myoelectric prosthetic hands that are controlled by a user’s remaining muscle activity. Fine finger movements are decoded from surface electromyograms with machine learning algorithms and this is combined with a robotic controller that is active only during object grasping to assist in maximizing contact. This shared control scheme allows user-controlled movements when high dexterity is desired, but also assisted grasping when robustness is required.

    • Katie Z. Zhuang
    • , Nicolas Sommer
    • , Vincent Mendez
    • , Saurav Aryan
    • , Emanuele Formento
    • , Edoardo D’Anna
    • , Fiorenzo Artoni
    • , Francesco Petrini
    • , Giuseppe Granata
    • , Giovanni Cannaviello
    • , Wassim Raffoul
    • , Aude Billard
    •  & Silvestro Micera
  • Article |

    Memristive devices can provide energy-efficient neural network implementations, but they must be tailored to suit different network architectures. Wang et al. develop a trainable weight-sharing mechanism for memristor-based CNNs and ConvLSTMs, achieving a 75% reduction in weights without compromising accuracy.

    • Zhongrui Wang
    • , Can Li
    • , Peng Lin
    • , Mingyi Rao
    • , Yongyang Nie
    • , Wenhao Song
    • , Qinru Qiu
    • , Yunning Li
    • , Peng Yan
    • , John Paul Strachan
    • , Ning Ge
    • , Nathan McDonald
    • , Qing Wu
    • , Miao Hu
    • , Huaqiang Wu
    • , R. Stanley Williams
    • , Qiangfei Xia
    •  & J. Joshua Yang
  • Article |

    Controlling the flow and representation of information in deep neural networks is fundamental to making networks intelligible. Bergomi et al introduce a mathematical framework in which the space of possible operators representing the data is constrained by using symmetries. This constrained space is still suitable for machine learning: operators can be efficiently computed, approximated and parameterized for optimization.

    • Mattia G. Bergomi
    • , Patrizio Frosini
    • , Daniela Giorgi
    •  & Nicola Quercioli
  • Article |

    An approach to protein structure prediction is to assemble candidate structures from template fragments, which are extracted from known protein structures. Wang et al. demonstrate that combining deep neural network architectures with a relatively small but high-resolution fragment dataset can improve the quality of the sample fragment libraries used for protein structure prediction.

    • Tong Wang
    • , Yanhua Qiao
    • , Wenze Ding
    • , Wenzhi Mao
    • , Yaoqi Zhou
    •  & Haipeng Gong
  • Article |

    When neural networks are retrained to solve more than one problem, they tend to forget what they have learned earlier. Here, the authors propose orthogonal weights modification, a method to avoid this so-called catastrophic forgetting problem. Capitalizing on such an ability, a new module is introduced to enable the network to continually learn context-dependent processing.

    • Guanxiong Zeng
    • , Yang Chen
    • , Bo Cui
    •  & Shan Yu
  • Article |

    Deep neural networks can contain arbitrary mathematical operators, as long as they are derivable. The authors investigate how knowledge about a problem can be incorporated into machine learning through the use of operators that are related to the problem.

    • Andreas K. Maier
    • , Christopher Syben
    • , Bernhard Stimpel
    • , Tobias Würfl
    • , Mathis Hoffmann
    • , Frank Schebesch
    • , Weilin Fu
    • , Leonid Mill
    • , Lasse Kling
    •  & Silke Christiansen
  • Article |

    For some combinatorial puzzles, solutions can be verified to be optimal, for others, the state space is too large to be certain that a solution is optimal. A new deep learning based search heuristic performs well on the iconic Rubik’s cube and can also generalize to puzzles in which optimal solvers are intractable.

    • Forest Agostinelli
    • , Stephen McAleer
    • , Alexander Shmakov
    •  & Pierre Baldi
  • Article |

    Neural networks are a promising digital pathology tool but are often criticized for their limited explainability. Faust and others demonstrate how machine-learned features correlate with human-understandable histological patterns and groupings, permitting increased transparency of deep learning tools in medicine.

    • Kevin Faust
    • , Sudarshan Bala
    • , Randy van Ommeren
    • , Alessia Portante
    • , Raniah Al Qawahmed
    • , Ugljesa Djuric
    •  & Phedias Diamandis
  • Article |

    Artificial intelligence approaches can aid medicinal chemists to creatively look for new chemical entities with drug-like properties. A rule-based approach combined with a machine learning model was trained on successful synthetic routes described in chemical patent literature. This process produced computer-generated compounds that mimic known medicines.

    • Alexander Button
    • , Daniel Merk
    • , Jan A. Hiss
    •  & Gisbert Schneider
  • Article |

    Reducing the radiation dose for medical CT scans can provide a less invasive imaging method, but requires a method for reconstructing an image up to the image quality from a full-dose scan. In this article, Wang and colleagues show that the deep learning approach, combined with the feedback from radiologists, produces higher quality reconstructions than or similar to that using the current commercial methods.

    • Hongming Shan
    • , Atul Padole
    • , Fatemeh Homayounieh
    • , Uwe Kruger
    • , Ruhani Doda Khera
    • , Chayanin Nitiwarangkul
    • , Mannudeep K. Kalra
    •  & Ge Wang