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General General

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification.

In Diagnostics (Basel, Switzerland)

Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.

Kasani Payam Hosseinzadeh, Park Sang-Won, Jang Jae-Won


acute lymphoblastic leukemia, computer-aided diagnosis, deep learning, transfer learning

oncology Oncology

A review on medical imaging synthesis using deep learning and its clinical applications.

In Journal of applied clinical medical physics ; h5-index 28.0

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

Wang Tonghe, Lei Yang, Fu Yabo, Wynne Jacob F, Curran Walter J, Liu Tian, Yang Xiaofeng


CT, MRI, PET, deep learning, image synthesis, radiation therapy

Radiology Radiology

Sub-2 mm Depth of Interaction Localization in PET Detectors with Prismatoid Light Guide Arrays and Single-Ended Readout using Convolutional Neural Networks.

In Medical physics ; h5-index 59.0

PURPOSE : Depth of interaction (DOI) readout in PET imaging has been researched in efforts to mitigate parallax error, which would enable the development of small diameter, high resolution PET scanners. However, DOI PET hasn't yet been commercialized due to the lack of practical, cost-effective and data efficient DOI readout methods. The rationale for this study was to develop a supervised machine learning algorithm for DOI estimation in PET that can be trained and deployed on unique sets of crystals.

METHODS : Depth collimated flood data was experimentally acquired using a Na-22 source with a depth-encoding single-ended readout Prism-PET module consisting of lutetium yttrium orthosilicate (LYSO) crystals coupled 4-to-1 to 3 x 3 mm2 silicon photomultiplier (SiPM) pixels on one end and a prismatoid light guide array on the other end. A convolutional neural network (CNN) was trained to perform DOI estimation on data from center, edge and corner crystals in the Prism-PET module using (a) all 64 readout pixels and (b) only the 4 highest readout signals per event. CNN testing was performed on data from crystals not included in CNN training.

RESULTS : An average DOI resolution of 1.84 mm full width at half maximum (FWHM) across all crystals was achieved when using all 64 readout signals per event with the CNN compared to 3.04 mm FWHM DOI resolution using classical estimation. When using only the 4 highest signals per event, an average DOI resolution of 1.92 mm FWHM was achieved, representing only a 4% dropoff in CNN performance compared to using all 64 pixels per event.

CONCLUSIONS : Our CNN-based DOI estimation algorithm provides the best reported DOI resolution in a single-ended readout module and can be readily deployed on crystals not used for model training.

LaBella Andy, Cao Xinjie, Zeng Xinjie, Zhao Wei, Goldan Amir H


CNN, DOI, Machine Learning, PET, Prism-PET

General General

Leveraging heterogeneous network embedding for metabolic pathway prediction.

In Bioinformatics (Oxford, England)

MOTIVATION : Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible.

RESULTS : Here, we present pathway2vec, a software package consisting of six representational learning modules used to automatically generate features for pathway inference. Specifically, we build a three-layered network composed of compounds, enzymes and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve prediction outcomes.

AVAILABILITY AND IMPLEMENTATION : The software package and installation instructions are published on


SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

M A Basher Abdur Rahman, Hallam Steven J


Pathology Pathology

ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages.

In Bioinformatics (Oxford, England)

MOTIVATION : Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a 'black box', barely providing biological and clinical interpretability from the box.

RESULTS : To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative-restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small-cell lung cancer patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. In addition, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (P-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine.

AVAILABILITYAND IMPLEMENTATION : ECMarker is open source as a general-purpose tool at


SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Jin Ting, Nguyen Nam D, Talos Flaminia, Wang Daifeng


General General

Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence.

In iScience

Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the "non-ideal" behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.

Wang Wei, Song Wenhao, Yao Peng, Li Yang, Van Nostrand Joseph, Qiu Qinru, Ielmini Daniele, Yang J Joshua


Computer Architecture, Hardware Co-design, Materials Science