High-level synthesis for medical image processing on Systems on Chip : a case study

Adaptive radiotherapy is a technique intended to increase the accuracy of radiotherapy. Currently, it is not clinically feasible due to the time required to process the images of patient anatomy. Hardware acceleration of image processing algorithms may allow them to be carried out in a clinically acceptable timeframe. This paper presents the experiences encountered using high-level synthesis tools to design an accelerated segmentation algorithm for computed tomography images targeted for implementation on a System on Chip. Hardware coprocessors and their interfaces for optimal threshold generation and 3D mean filter algorithms were synthesised from C++ functions. Hardware acceleration significantly outperformed the software only implementation. The high-level synthesis tools allowed the rapid exploration of different design options. However, hardware design knowledge was still necessary in order to interpret the results effectively

COIN:Contrastive Identifier Network for Breast Mass Diagnosis in Mammography

Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement. In particular, data scarcity is attributed to the privacy and expensive annotation. And data entanglement is due to the high similarity between benign and malignant masses, of which manifolds reside in lower dimensional space with very small margin. To address these two challenges, we propose a deep learning framework, named Contrastive Identifier Network (\textsc{COIN}), which integrates adversarial augmentation and manifold-based contrastive learning. Firstly, we employ adversarial learning to create both on- and off-distribution mass contained ROIs. After that, we propose a novel contrastive loss with a built Signed graph. Finally, the neural network is optimized in a contrastive learning manner, with the purpose of improving the deep model's discriminativity on the extended dataset. In particular, by employing COIN, data samples from the same category are pulled close whereas those with different labels are pushed further in the deep latent space. Moreover, COIN outperforms the state-of-the-art related algorithms for solving breast cancer diagnosis problem by a considerable margin, achieving 93.4\% accuracy and 95.0\% AUC score. The code will release on ***

A Deep DUAL-PATH Network for Improved Mammogram Image Processing

We present, for the first time, a novel deep neural network architecture called \dcn with a dual-path connection between the input image and output class label for mammogram image processing. This architecture is built upon U-Net, which non-linearly maps the input data into a deep latent space. One path of the \dcnn, the locality preserving learner, is devoted to hierarchically extracting and exploiting intrinsic features of the input, while the other path, called the conditional graph learner, focuses on modeling the input-mask correlations. The learned mask is further used to improve classification results, and the two learning paths complement each other. By integrating the two learners our new architecture provides a simple but effective way to jointly learn the segmentation and predict the class label. Benefiting from the powerful expressive capacity of deep neural networks a more discriminative representation can be learned, in which both the semantics and structure are well preserved. Experimental results show that \dcn achieves the best mammography segmentation and classification simultaneously, outperforming recent state-of-the-art models.Comment: To Appear in ICCASP 2019 Ma

Measuring the effects of fractionated radiation therapy in a 3D prostate cancer model system using SERS nanosensors.

Multicellular tumour spheroids (MTS) are three-dimensional cell cultures that possess their own microenvironments and provide a more meaningful model of tumour biology than monolayer cultures. As a result, MTS are becoming increasingly used as tumor models when measuring the efficiency of therapies. Monitoring the viability of live MTS is complicated by their 3D nature and conventional approaches such as fluorescence often require fixation and sectioning. In this paper we detail the use of Surface Enhanced Raman Spectroscopy (SERS) to measure the viability of MTS grown from prostate cancer (PC3) cells. Our results show that we can monitor loss of viability by measuring pH and redox potential in MTS and furthermore we demonstrate that SERS can be used to measure the effects of fractionation of a dose of radiotherapy in a way that has potential to inform treatment planning.EaStCHEM, NHS Lothian, Jamie King Cancer Research FundThis is the final version of the article. It first appeared from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C6AN01032

VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography - Mass Spectrometry Data

<div>Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC-MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC-MS breath with similar mass spectra and retention index profiles.</div

Fast and automated biomarker detection in breath samples with machine learning

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency

From multisource data to clinical decision aids in radiation oncology:The need for a clinical data science community

Big data are no longer an obstacle; now, by using artificial intelligence (AI), previously undiscovered knowledge can be found in massive data collections. The radiation oncology clinic daily produces a large amount of multisource data and metadata during its routine clinical and research activities. These data involve multiple stakeholders and users. Because of a lack of interoperability, most of these data remain unused, and powerful insights that could improve patient care are lost. Changing the paradigm by introducing powerful AI analytics and a common vision for empowering big data in radiation oncology is imperative. However, this can only be achieved by creating a clinical data science community in radiation oncology. In this work, we present why such a community is needed to translate multisource data into clinical decision aids

Targeted SERS nanosensors measure physicochemical gradients and free energy changes in live 3D tumor spheroids.

Use of multicellular tumor spheroids (MTS) to investigate therapies has gained impetus because they have potential to mimic factors including zonation, hypoxia and drug-resistance. However, analysis remains difficult and often destroys 3D integrity. Here we report an optical technique using targeted nanosensors that allows in situ 3D mapping of redox potential gradients whilst retaining MTS morphology and function. The magnitude of the redox potential gradient can be quantified as a free energy difference (ΔG) and used as a measurement of MTS viability. We found that by delivering different doses of radiotherapy to MTS we could correlate loss of ΔG with increasing therapeutic dose. In addition, we found that resistance to drug therapy was indicated by an increase in ΔG. This robust and reproducible technique allows interrogation of an in vitro tumor-model's bioenergetic response to therapy, indicating its potential as a tool for therapy development.Leverhulme Trust (Grant ID: RPG-2012-680), Jamie King Cancer Research FundThis is the final version of the article. It first appeared from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C6NR06031