Multi-task Deep Neural Network Research Articles

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.

Robust returns ranking prediction and portfolio optimization for M6

The M6 competition aims to address challenging problems in stock returns ranking prediction and portfolio optimization. To tackle the volatility and low signal-to-noise ratio in the stock market, our team designs the overall solution from the perspective of robustness. On the task of returns ranking prediction, we present the MultiTask Deep Neural Network with Denoising Autoencoder Enhancement (MT-DNN-DAE), which incorporates the self-supervised learning of DAE and jointly optimizes the multi-task loss. Additionally, we propose Robust Feature Selection (RFS) to identify features with a high signal-to-noise ratio for DAE’s representation learning and construct a separate branch for important ID feature to prevent its information loss. Results show our solution is capable of accurately predicting returns ranking while maintaining generalization. On the task of portfolio optimization, a Differential Evolution algorithm is also presented to optimize asset allocation and maximize returns under risk constraints, demonstrating improved performance over traditional techniques. Taken together, these methods led to a 4th place global ranking in the M6 competition.

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning

BackgroundClassifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes.ResultsIn this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data.ConclusionsThe experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

Abstract 7396: Development of deep neural networks that guide more individualized radiotherapy to the lung

Abstract Introduction: Radiotherapy is dependent on an expert-based model that impedes implementation of many potentially useful applications that could aid decision makers. This model is a barrier to a learning healthcare system due to noise introduced by decision-maker variation. Surmounting this barrier will require compelling clinical outcome data and highly useful and accurate information systems. We sought to develop new augmented-intelligence capabilities to better predict both high-dose lung irradiation treatment efficacy (i.e., local tumor control) and toxicity (i.e., radiation-induced pneumonitis). Methods: We curated one of the largest datasets of lung stereotactic body radiotherapy (SBRT) datasets globally (~1200 cases). We input pre-therapy lung computed tomography (CT) images using a bounding box approach that captures the gross tumor volume and adjacent normal lung into a multi-task deep neural network to generate an image fingerprint that predicts local failures after treatment. Separately, we input axial slices from CT scans of the lung into convolutional neural network (CNN) and trained using an outcome per slice method. We then aggregated intra-patient slices into a recurrent neural network (RNN) to estimate individual patient risk for pneumonitis. We validated our findings with the independent study cohort. Risk scores for local failure and pneumonitis were combined with clinical variables to assess the relative contribution of the deep learning-derived image risk scores. Results: A total of 1045 patients met our eligibility criteria. Radiation treatments in patients with high local failure risk scores failed at a significantly higher rate than in patients with low scores; 3-year cumulative incidence of local failure in the internal study cohort was 20·3% (16·0-24·9) in patients with high scores and 5·7% (95% CI 3·5-8·8) in patients with low scores (hazard ratio [HR]=3·64 [95% CI 2·19-6·05], p<0·0001). Models that included the local failure risk score and clinical variables predicted treatment failures with a concordance index (C-index) of 0·72 (95% CI 0·67-0·77), a significant improvement compared with classical radiomics (p<0·0001) or clinical variables (p<0·0001) alone. Radiation pneumonitis risk scores were highly predictive of toxicity, with an accuracy and CI of 0.78 (95% CI 0.72-0.85). A model that includes dosimetry variates (V20), improved the CI to 0.85 (95% CI 0.80-0.90). Conclusions: The image-based deep learning frameworks developed herein represent the first opportunity to use medical images to individualize radiotherapy dose and toxicity predictions. Our results signify a new roadmap for deep learning-guided predictions and treatment guidance in the image-replete and highly standardized discipline of radiation oncology. A phase 2 study assessing machine-derived dose predictions is currently enrolling patients. Citation Format: Mohamed Abazeed. Development of deep neural networks that guide more individualized radiotherapy to the lung [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7396.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

BackgroundPredicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information.ResultsThe model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study.ConclusionsOur proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet.

Deep survival analysis model for incident clearance time prediction

Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers “duration dependence” of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.

Abstract B082: Early risk stratification of ER+/HER2– breast cancer patients using digital pathology and multi-task, weakly-supervised deep learning

Abstract Breast cancer prognostication guides treatment decisions, with transcriptomic assays like Prosigna and Oncotype DX providing valuable risk of recurrence (ROR) scores in ER+/HER2– patients. However, these tests are costly and require sufficient biopsy tissue for accuracy. Pathology images are routinely available and could expedite rapid risk stratification at lower cost. We propose a multi-modal, multi-task deep neural network that learns transcriptomic ROR using digital pathology images and hormone receptor expression status. Because the incorporation of both pathology images and clinical data has proven beneficial in other deep learning models, we hypothesized that incorporation of this complementary information would improve ROR predictions compared to using pathology images alone. We modified the “clustering-constrained-attention multiple-instance learning” (CLAM) deep learning method to perform multi-task regression and infer continuous risk scores (instead of discrete risk categories). We evaluated (1) PAM50 ROR-P, calculated using a multivariate model based on intrinsic subtype centroids and a proliferation score, and (2) a 21-gene assay recurrence score (RS) (research version of Oncotype DX), calculated from component scores relating to proliferation, ER, HER2, and invasion. Using data from 899 patients in the Carolina Breast Cancer Study, a population-based study of diverse patients including 50% Black women and 50% women under age 50, we constructed training (n=714), validation (n=93), and test (n=92) sets. Genomic data were assayed by Nanostring, and digital pathology was based on H&E-stained whole slide images (WSIs). Models were assessed using Pearson correlation between measured and predicted ROR-P or RS. The model with the highest correlations on the test set used H&E WSIs and ER/PR/HER2 expression status as inputs and generated 10 independent outputs: ROR-P, four PAM50 centroid correlations (i.e., Basal, HER2-enriched, Luminal A, and Luminal B), RS, and four RS component scores. Prediction was strong when considering all participants in the test set (ROR-P, 0.78; RS, 0.83, N, 92) but was reduced among more clinically homogeneous ER+/HER2– patients (ROR-P, 0.55; RS, 0.49; N, 49). For RS, correlations were higher in models that included ER/PR/HER2 (Overall, 0.83; ER+/HER2–, 0.50) than in those that relied on pathology alone (Overall, 0.60, ER+/HER2–, 0.34). Future models will consider strategies to enhance performance in ER+/HER2– patients, such as oversampling of ER+/HER2– patients and use of loss functions designed to focus learning on mis-predicted examples. In summary, multi-task, tissue-based regression deep learning models recapitulate transcription-based risk assays with high correlations and, with optimization (especially integration of additional clinicopathologic data), hold promise for personalized treatment decisions from early, routinely-collected biopsy images. Citation Format: Jakub R Kaczmarzyk, Luke A Torre-Healy, Richard A Moffitt, Rajarsi Gupta, Alina M Hamilton, Tahsin M Kurc, Katherine A Hoadley, Melissa A Troester, Joel H Saltz. Early risk stratification of ER+/HER2– breast cancer patients using digital pathology and multi-task, weakly-supervised deep learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B082.

Refined image quality assessment for color fundus photography based on deep learning.

Color fundus photography is widely used in clinical and screening settings for eye diseases. Poor image quality greatly affects the reliability of further evaluation and diagnosis. In this study, we developed an automated assessment module for color fundus photography image quality assessment using deep learning. A total of 55,931 color fundus photography images from multiple centers in Shanghai and the public database were collected and annotated as training, validation, and testing data sets. The pre-diagnosis image quality assessment module based on the multi-task deep neural network was designed. The detailed criterion of color fundus photography image quality including three subcategories with three levels of grading was applied to improve precision and objectivity. The auxiliary tasks such as the localization of the optic nerve head and macula, the classification of laterality, and the field of view were also included to assist the quality assessment. Finally, we validated our module internally and externally by evaluating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and quadratic weighted Kappa. The "Location" subcategory achieved area under the receiver operating characteristic curves of 0.991, 0.920, and 0.946 for the three grades, respectively. The "Clarity" subcategory achieved area under the receiver operating characteristic curves of 0.980, 0.917, and 0.954 for the three grades, respectively. The "Artifact" subcategory achieved area under the receiver operating characteristic curves of 0.976, 0.952, and 0.986 for the three grades, respectively. The accuracy and Kappa of overall quality reach 88.15% and 89.70%, respectively, on the internal set. These two indicators on the external set were 86.63% and 88.55%, respectively, which were very close to that of the internal set. This work showed that our deep module was able to evaluate the color fundus photography image quality using more detailed three subcategories with three grade criteria. The promising results on both internal and external validation indicated the strength and generalizability of our module.

Multitask Deep Neural Network with Knowledge-Guided Attention for Blind Image Quality Assessment

Multitask Deep Neural Network with Knowledge-Guided Attention for Blind Image Quality Assessment

A Multi-task Deep Feature Selection Method for Brain Imaging Genetics.

Using brain imaging quantitative traits (QTs) for identifying genetic risk factors is an important research topic in brain imaging genetics. Many efforts have been made for this task via building linear models between imaging QTs and genetic factors such as single nucleotide polymorphisms (SNPs). To the best of our knowledge, linear models could not fully uncover the complicated relationship due to the loci's elusive and diverse influences on imaging QTs. In this paper, we propose a novel multi-task deep feature selection (MTDFS) method for brain imaging genetics. MTDFS first builds a multi-task deep neural network to model the complicated associations between imaging QTs and SNPs. And then designs a multi-task one-to-one layer and imposes a combined penalty to identify SNPs that make significant contributions. MTDFS can not only extract the nonlinear relationship but also arms the deep neural network with feature selection. We compared MTDFS to multi-task linear regression (MTLR) and single-task DFS (DFS) methods on the real neuroimaging genetic data. The experimental results showed that MTDFS performed better than MTLR and DFS on the QT-SNP relationship identification and feature selection. Thus, MTDFS is powerful for identifying risk loci and could be a great supplement to brain imaging genetics.

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images

Objective. Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a computed tomography (CT) image-based hybrid deep radiomics model to predict EGFR mutation status in NSCLC and investigate the correlations between deep image and quantitative radiomics features. Approach. First, we retrospectively enrolled 818 patients from our centre and 131 patients from The Cancer Imaging Archive database to establish a training cohort (N = 654), an independent internal validation cohort (N = 164) and an external validation cohort (N = 131). Second, to predict EGFR mutation status, we developed three CT image-based models, namely, a multi-task deep neural network (DNN), a radiomics model and a feature fusion model. Third, we proposed a hybrid loss function to train the DNN model. Finally, to evaluate the model performance, we computed the areas under the receiver operating characteristic curves (AUCs) and decision curve analysis curves of the models. Main results. For the two validation cohorts, the feature fusion model achieved AUC values of 0.86 ± 0.03 and 0.80 ± 0.05, which were significantly higher than those of the single-task DNN and radiomics models (all P < 0.05). There was no significant difference between the feature fusion and the multi-task DNN models (P > 0.8). The binary prediction scores showed excellent prognostic value in predicting disease-free survival (P = 0.02) and overall survival (P < 0.005) for validation cohort 2. Significance. The results demonstrate that (1) the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-task DNN models, (2) the feature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSCLC prognosis, and (3) high correlations exist between some deep image and radiomics features.

MTD-YOLO: Multi-task deep convolutional neural network for cherry tomato fruit bunch maturity detection

In recent years, the escalating labor costs in agricultural production have emerged as a major concern. The use of inspection robots to achieve automated inspection of fruit and fruit bunches for ripeness not only enhances production efficiency and cost savings, but also simplifies the tasks for workers. To address this issue, an improved YOLOv7-based multi-task deep convolutional neural network (DCNN) detection model, called MTD-YOLOv7, is proposed in this paper. Initially, the dataset labels were expanded to meet the requirements of multi-task classification. Two additional decoders were then added on the basis of YOLOv7 to detect tomato fruit clusters, fruit maturity and cluster maturity. Subsequently, the loss function was designed based on the characteristics of multi-task and the Scale-Sensitive Intersection over Union (SIoU) was used instead of Complete Intersection over Union (CIoU) to improve the model’s recognition accuracy. Finally, to verify the effectiveness of the algorithm, tests were conducted on the cherry tomato dataset, and comparisons were made with common target detection algorithms, classification models, and cascade models. The experimental findings reveal that MTD-YOLOv7 achieved an overall score of 86.6% in multi-task learning, with an average inference time of 4.9 ms (RTX3080). It excels in simultaneous detection of cherry tomato fruits and bunches, fruit maturity, and bunch maturity, offering exceptional precision, rapid detection, and robust generalization capabilities. Its suitability extends to various applications, notably in inspection tasks.

Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3T MRI.

Precise delineation of hippocampus subfields is crucial for the identification and management of various neurological and psychiatric disorders. However, segmenting these subfields automatically in routine 3T MRI is challenging due to their complex morphology and small size, as well as the limited signal contrast and resolution of the 3T images. This research proposes Syn_SegNet, an end-to-end, multitask joint deep neural network that leverages ultrahigh-field 7T MRI synthesis to improve hippocampal subfield segmentation in 3T MRI. Our approach involves two key components. First, we employ a modified Pix2PixGAN as the synthesis model, incorporating self-attention modules, image and feature matching loss, and ROI loss to generate high-quality 7T-like MRI around the hippocampal region. Second, we utilize a variant of 3D-U-Net with multiscale deep supervision as the segmentation subnetwork, incorporating an anatomic weighted cross-entropy loss that capitalizes on prior anatomical knowledge. We evaluate our method on hippocampal subfield segmentation in paired 3T MRI and 7T MRI with seven different anatomical structures. The experimental findings demonstrate that Syn_SegNet's segmentation performance benefits from integrating synthetic 7T data in an online manner and is superior to competing methods. Furthermore, we assess the generalizability of the proposed approach using a publicly accessible 3T MRI dataset. The developed method would be an efficient tool for segmenting hippocampal subfields in routine clinical 3T MRI.

Range estimation in a weakly range-dependent waveguide in the South China Sea using single hydrophone and multi-task trained deep neural network

In this paper, an underwater source range estimation method using a multi-task trained deep neural network is proposed. Unlike existing machine learning frameworks, in this case, the physical quantities utilized for the training and testing are not identical. The training data parameter is the phase of the complex sound pressure, which is independent of the source and receiver depth and can be generated easily by an acoustic propagation model with only a few environmental parameters, decreasing computational costs significantly. The testing data parameter is the realistically-measured complex sound pressure. Instead of simply using the range as the only supervision signal, two other parameters — the phase mode and the bottom sound speed — are applied as auxiliary labels during the training process, embedding additional information about the sound field into the deep model. The projection between the sound field features and the range extracted from the training data by the deep model is applied to range estimation task on testing data. The effectiveness of the proposed approach is verified for a weakly range-dependent waveguide in the South China Sea by localizing several upsweep signals within a frequency band of 20–200 Hz. Testing results illustrate that the proposed method outperforms the classical matched impulse response approach and deep-learning-based single-label trained models, with the mean absolute percentage error (MAPE) decreasing by 24.63% and 59.46%, respectively. The mechanism of promotion is explained by comparing visualized hidden features of the neural network using t-distributed stochastic neighbor embedding (t-SNE) between the single-task and multi-task approach, showing that the latter helps to construct a more reasonable data manifold in high-dimensional space and thus ensures better performance of the deep model. We also fine-tune the network with a few realistic samples and further improve the performance by up to 25.34% against the baseline.

Nonlinear constitutive calculation method of rarefied flow based on deep convolution neural networks

In the field of rarefied gas dynamics, the presence of non-equilibrium flow characteristics poses significant challenges for achieving efficient and accurate numerical simulation methods. These challenges arise from the complex coexistence of these phenomena at multiple scales. The recent advent of intelligent fluid mechanics has introduced the data-driven nonlinear constitutive relation (DNCR) method as a promising approach for expeditious physical modeling of non-equilibrium rarefied flows. To enhance the generalization capabilities of the DNCR method, this study proposes a deep convolutional neural network model (DNCR-CNN) based on data-driven nonlinear constitutive relations, integrated with free-form deformation (FFD). Employing FFD technology, a series of hypersonic geometric shapes are generated for model training, and a multi-task learning-based deep convolutional neural network model is subsequently trained. The prediction of the hypersonic geometric shapes test set is carried out, and the results of the model prediction are substituted in the conservation equation for the iterative solution, thereby enhancing the DNCR method's generalization performance for varying geometric shapes. Upon conducting a comparative analysis of the outcomes obtained from DNCR, Navier–Stokes (NS), and unified gas kinetic scheme (UGKS), it is revealed that the DNCR method can maintain computational resource levels equivalent to those of the NS equation while achieving a level of accuracy comparable to UGKS under diverse geometric shapes and grid resolutions. The enhancements in usability render the DNCR method a potent tool for addressing the challenges posed by rarefied gas, thereby expanding its applicability within the field.

GlassNet: A multitask deep neural network for predicting many glass properties

A multitask deep neural network model was trained on more than 218k different glass compositions. This model, called GlassNet, can predict 85 different properties (such as optical, electrical, dielectric, mechanical, and thermal properties, as well as density, viscosity/relaxation, crystallization, surface tension, and liquidus temperature) of glasses and glass-forming liquids of different chemistries (such as oxides, chalcogenides, halides, and others). The model and the data used to train it are available in the GlassPy Python module as free and open source software for the community to use and build upon. As a proof of concept, GlassNet was used with the MYEGA viscosity equation to predict the temperature dependence of viscosity and outperformed another general purpose viscosity model available in the literature (ViscNet) on unseen data. An explainable AI algorithm (SHAP) was used to extract knowledge correlating the input (physicochemical information) and output (glass properties) of the model, providing valuable insights for glass manufacturing and design. It is hoped that GlassNet, with its free and open source nature, can be used to enable faster and better computer-aided design of new technological glasses.

Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach

The prediction and behavioural analysis of travel mode choice and purpose are critical for transport planning and have attracted increasing interest in research. Traditionally, the prediction of travel mode choice and trip purpose has been tackled separately, which fail to fully leverage the shared information between travel mode and purpose. This study addresses this gap by proposing a multitask learning deep neural network framework (MTLDNN) to jointly predict mode choice and purpose. We empirically evaluate and validate this framework using the household travel survey data in Greater London, UK. The results show that this framework has significantly lower cross-entropy loss than multinomial logit models (MNL) and single-task-learning deep neural network models (STLDNN). On the other hand, the predictive accuracy of MTLDNN is similar to STLDNN and is significantly higher than MNL. Moreover, in terms of behaviour analysis, the substitution pattern and choice probability of MTLDNN regarding input variables largely agree with MNL and STLDNN. This work demonstrates that MTLDNN is efficient in utilising the information shared by travel mode choice and purpose, and is capable of producing behaviourally reasonable substitution patterns across travel modes. Future research would develop more advanced MTLDNN frameworks for travel behaviour analysis and generalise MTLDNN to other travel behaviour topics.

A Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system

Cities are facing numerous challenges such as road traffic congestion and environmental pollution. Bike-sharing, as an emission-free travel mode, aligns with the principle of green and environmental protection and serves mainly for short-distance trips in urban areas. Shared bikes pose significant issues to operators due to supply–demand imbalances across different time and space. This study proposes a Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system, which aims to minimize the cost of fuel and carbon emissions from repositioning vehicles and the total unmet demand during the operating period. A multi-task deep neural network model is designed to predict regional inflow and outflow demand, where targeted modules are embedded to extract the spatio-temporal characteristics. Potential unusable shared bikes are discovered from the users’ travel behavior and the usage characteristics of shared bikes. Then, we build a data-driven optimization model for bike-sharing relocation and design an iterative decomposition algorithm that incorporates an adaptive large neighborhood search for relocation routes and vehicle speed optimization. The proposed method is tested on real-world bike-sharing trips in Shenzhen, China, and results show that the relocation distance and carbon emission cost can be reduced by 11.69% and 14.09% by relocating operational bikes and unusable bikes simultaneously. Additionally, route decision-making with speed optimization can decrease the total fuel and emission, where considering the collection of unusable bikes help improve the service level of the system.

Explainable multi-task learning for multi-modality biological data analysis

Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.

MO-0959 A novel multi-task hybrid deep neural network (DNN) predicts tumor progression during MRgRT

MO-0959 A novel multi-task hybrid deep neural network (DNN) predicts tumor progression during MRgRT