Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

A Machine Learning Approach to Identifying Changes in Suicidal Language.

In Suicide & life-threatening behavior

OBJECTIVE : With early identification and intervention, many suicidal deaths are preventable. Tools that include machine learning methods have been able to identify suicidal language. This paper examines the persistence of this suicidal language up to 30 days after discharge from care.

METHOD : In a multi-center study, 253 subjects were enrolled into either suicidal or control cohorts. Their responses to standardized instruments and interviews were analyzed using machine learning algorithms. Subjects were re-interviewed approximately 30 days later, and their language was compared to the original language to determine the presence of suicidal ideation.

RESULTS : The results show that language characteristics used to classify suicidality at the initial encounter are still present in the speech 30 days later (AUC = 89% (95% CI: 85-95%), p < .0001) and that algorithms trained on the second interviews could also identify the subjects that produced the first interviews (AUC = 85% (95% CI: 81-90%), p < .0001).

CONCLUSIONS : This approach explores the stability of suicidal language. When using advanced computational methods, the results show that a patient's language is similar 30 days after first captured, while responses to standard measures change. This can be useful when developing methods that identify the data-based phenotype of a subject.

Pestian John, Santel Daniel, Sorter Michael, Bayram Ulya, Connolly Brian, Glauser Tracy, DelBello Melissa, Tamang Suzanne, Cohen Kevin


oncology Oncology

Automatic Multi-Catheter Detection using Deeply Supervised Convolutional Neural Network in MRI-guided HDR Prostate Brachytherapy.

In Medical physics ; h5-index 59.0

PURPOSE : High-dose-rate (HDR) brachytherapy is an established technique to be used as monotherapy option or focal boost in conjunction with external beam radiation therapy (EBRT) for treating prostate cancer. Radiation source path reconstruction is a critical procedure in HDR treatment planning. Manually identifying the source path is labor intensive and timely inefficient. Recent years, magnetic resonance imaging (MRI) becomes a valuable imaging modality for image-guided HDR prostate brachytherapy due to its superb soft-tissue contrast for target delineation and normal tissue contouring. The purpose of this study is to investigate a deep-learning-based method to automatically reconstruct multiple catheters in MRI for prostate cancer HDR brachytherapy treatment planning.

METHODS : Attention gated U-Net incorporated with total variation (TV) regularization model was developed for multi-catheter segmentation in MRI. The attention gates were used to improve the accuracy of identifying small catheter points, while TV regularization was adopted to encode the natural spatial continuity of catheters into the model. The model was trained using the binary catheter annotation images offered by experienced physicists as ground truth paired with original MRI images. After the network was trained, MR images of a new prostate cancer patient receiving HDR brachytherapy were fed into the model to predict the locations and shapes of all the catheters. Quantitative assessments of our proposed method were based on catheter shaft and tip errors compared to the ground truth.

RESULTS : Our method detected 299 catheters from 20 patients receiving HDR prostate brachytherapy with a catheter tip error of 0.37±1.68 mm and a catheter shaft error of 0.93±0.50 mm. For detection of catheter tips, our method resulted in 87% of the catheter tips within an error of less than ±2.0 mm, and more than 71% of the tips can be localized within an absolute error of no greater than 1.0 mm. For catheter shaft localization, 97% of catheters were detected with an error of less than 2.0 mm, while 63% were within 1.0 mm.

CONCLUSIONS : In this study, we proposed a novel multi-catheter detection method to precisely localize the tips and shafts of catheters in 3D MRI images of HDR prostate brachytherapy. It paves the way for elevating the quality and outcome of MRI-guided HDR prostate brachytherapy.

Dai Xianjin, Lei Yang, Zhang Yupei, Qiu Richard L J, Wang Tonghe, Dresser Sean A, Curran Walter J, Patel Pretesh, Liu Tian, Yang Xiaofeng


Magnetic resonance imaging, catheter detection, deep learning, prostate brachytherapy, total variation regularization

General General

Morphological traits of drought tolerant Horse gram germplasm: Classification through machine learning.

In Journal of the science of food and agriculture

BACKGROUND : Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with high drought resistance traits and rich source of protein. But, conventional breeding method for high yielding and abiotic stress tolerant germplasm is hampered by the scarcity of morphological data sets. Therefore, classification of Horse gram adapted to various agro-ecological zones prevailing various stress factors to exhibit homogenous genotype. Nowadays, several machine learning (ML) methods were used in the field of plant phenotyping.

RESULTS : Here we adopted unsupervised learning techniques of K-means clustering algorithm for their usefulness to analyze six important morphological traits such as plant shoot length, total plant height, flowering percentage, number of pods per plant, pod length, number of seeds per plant and seed length variants between germplasm. Unsupervised clustering revealed that, twenty germplasm accessions were grouped in four clusters in which high yielding trait was predominantly observed in the cluster 2.

CONCLUSION : Therefore, these findings could guide ML based classification easily to characterize the suitable germplasm on the basis of high yielding variety for the different agro-ecological zones. This article is protected by copyright. All rights reserved.

Amal Thomas Cheeran, Thottathil Asif T, Veerakumari KumarasamyPradeepa, Rakkiyappan Rajan, Vasanth Krishnan


Germplasm, Horse gram, K-means Clustering, Machine learning, Morphological traits

General General

Beware of the generic machine learning-based scoring functions in structure-based virtual screening.

In Briefings in bioinformatics

Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for generic uses rather than a given target can consistently be applicable for VS. In this study, a systematic assessment was carried out to re-evaluate the effectiveness of 14 reported MLSFs in VS. Overall, most of these MLSFs could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP. An exception was observed for RFscore-VS trained on the Directory of Useful Decoys-Enhanced dataset, which showed its superiority for most targets. However, in most cases, it clearly illustrated rather limited performance on the targets that were dissimilar to the proteins in the corresponding training sets. We also used the top three docking poses rather than the top one for rescoring and retrained the models with the updated versions of the training set, but only minor improvements were observed. Taken together, generic MLSFs may have poor generalization capabilities to be applicable for the real VS campaigns. Therefore, it should be quite cautious to use this type of methods for VS.

Shen Chao, Hu Ye, Wang Zhe, Zhang Xujun, Pang Jinping, Wang Gaoang, Zhong Haiyang, Xu Lei, Cao Dongsheng, Hou Tingjun


machine learning, machine learning-based scoring function, scoring function, virtual screening

General General

Deep learning applied to polysomnography to predict blood pressure in obstructive sleep apnea and obesity hypoventilation: a proof-of-concept study.

In Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

STUDY OBJECTIVES : Nocturnal blood pressure (BP) profile shows characteristic abnormalities in obstructive sleep apnea (OSA), namely acute post-apnea BP surges and non-dipping BP. These abnormal BP profiles provide prognostic clues indicating increased cardiovascular disease (CVD) risk. We developed a deep neural network model to perform computerized analysis of polysomnography data and predict nocturnal BP profile.

METHODS : We analyzed concurrently performed polysomnography and non-invasive beat-to-beat BP measurement with a deep neural network model to predict nocturnal BP profiles from polysomnography data in thirteen patients with severe obstructive sleep apnea.

RESULTS : A good correlation was noted between measured and predicted post-apnea systolic and diastolic BP (Pearson's r ≥ 0.75). Moreover, Bland Altman analyses showed good agreement between the two values. Continuous systolic and diastolic BP prediction by the DNN model was also well-correlated with measured BP values (Pearson's r ≥ 0.83).

CONCLUSIONS : We developed a deep neural network model to predict nocturnal BP profile from clinical polysomnography signals and provide a potential prognostic tool in OSA. Validation of the model in larger samples and examination of its utility in predicting CVD risk in future studies is warranted.

Prasad Bharati, Agarwal Chirag, Schonfeld Elan, Schonfeld Dan, Mokhlesi Babak


blood pressure, deep neural network, obstructive sleep apnea

General General

SRN: Side-Output Residual Network for Object Reflection Symmetry Detection and Beyond.

In IEEE transactions on neural networks and learning systems

This article establishes a baseline for object reflection symmetry detection in natural images by releasing a new benchmark named Sym-PASCAL and proposing an end-to-end deep learning approach for reflection symmetry. Sym-PASCAL spans challenges of multiobjects, object diversity, part invisibility, and clustered backgrounds, which is far beyond those in existing data sets. The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the symmetry ground truth and the side outputs of multiple stages of a trunk network. By cascading RUs from deep to shallow, SRN exploits the ``flow'' of errors along multiple stages to effectively matching object symmetry at different scales and suppress the clustered backgrounds. SRN is interpreted as a boosting-like algorithm, which assembles features using RUs during network forward and backward propagations. SRN is further upgraded to a multitask SRN (MT-SRN) for joint symmetry and edge detection, demonstrating its generality to image-to-mask learning tasks. Experimental results verify that the Sym-PASCAL benchmark is challenging related to real-world images, SRN achieves state-of-the-art performance, and MT-SRN has the capability to simultaneously predict edge and symmetry mask without loss of performance.

Ke Wei, Chen Jie, Jiao Jianbin, Zhao Guoying, Ye Qixiang