Emergency Department Pediatric Readiness Among US Trauma Centers: A Machine Learning Analysis of Components Associated With Survival.

OBJECTIVE: We used machine learning to identify the highest impact components of emergency department (ED) pediatric readiness for predicting in-hospital survival among children cared for in US trauma centers. BACKGROUND: ED pediatric readiness is associated with improved short-term and long-term survival among injured children and part of the national verification criteria for US trauma centers. However, the components of ED pediatric readiness most predictive of survival are unknown. METHODS: This was a retrospective cohort study of injured children below 18 years treated in 458 trauma centers from January 1, 2012, through December 31, 2017, matched to the 2013 National ED Pediatric Readiness Assessment and the American Hospital Association survey. We used machine learning to analyze 265 potential predictors of survival, including 152 ED readiness variables, 29 patient variables, and 84 ED-level and hospital-level variables. The primary outcome was in-hospital survival. RESULTS: There were 274,756 injured children, including 4585 (1.7%) who died. Nine ED pediatric readiness components were associated with the greatest increase in survival: policy for mental health care (+8.8% change in survival), policy for patient assessment (+7.5%), specific respiratory equipment (+7.2%), policy for reduced-dose radiation imaging (+7.0%), physician competency evaluations (+4.9%), recording weight in kilograms (+3.2%), life support courses for nursing (+1.0%-2.5%), and policy on pediatric triage (+2.5%). There was a 268% improvement in survival when the 5 highest impact components were present. CONCLUSIONS: ED pediatric readiness components related to specific policies, personnel, and equipment were the strongest predictors of pediatric survival and worked synergistically when combined.

Regional strain determination and myocardial infarction detection by three-dimensional echocardiography with varied temporal resolution.

BACKGROUND: Three-dimensional echocardiography (3DE) is a promising method for strain determination; however, there are temporal resolution concerns. This study aims to evaluate the feasibility and accuracy of 3DE on longitudinal and circumferential strain (LS, CS) determination and infarction detection under variable frame rates (FR) and "heart rates" (stroke rates [SR]) conditions. METHODS: Latex balloons were sewn into the left ventricle (LV) of 20 freshly harvested pig hearts which were then passively driven by a pulsatile pump apparatus at stroke volumes (SV) 30-70 mL. The hearts were pumped at 2 normal limits of human heart rate. Full-volume data were acquired before and after a simulated myocardial infarction (MI) at the 2 most commonly used FRs. LS and CS values were evaluated against sonomicrometry. RESULTS: Longitudinal strain and CS derived from high FR acquisitions showed statistically superior correlations with sonomicrometry data (LS: R(2) = 0.85, CS: R(2) = 0.84) than strain values from low FR (LS: R(2) = 0.78, CS: R(2) = 0.76) (all P < 0.01). After MI induction, LS and CS at different FRs were significantly decreased while maintaining excellent correlations with sonomicrometry data (all P < 0.001). There is no statistical difference of strain values between different SR acquisitions. CONCLUSION: Three-dimensional wall-motion tracking has the ability to accurately determine regional myocardial deformation and detect MI. Different heart rates within a physiologically relevant range have no effect on 3D strain accuracy. Strain values calculated from higher frame rate acquisitions were found to have a slightly better accuracy.

Real Time Three-Dimensional Echocardiographic Evaluations of Fetal Left Ventricular Stroke Volume, Mass, and Myocardial Strain: In Vitro and In Vivo Experimental Study.

BACKGROUND: Left ventricular stroke volume, mass, and myocardial strain are valuable indicators of fetal heart function. This study investigated the feasibility of nongated real time three-dimensional echocardiography (RT3DE) to determine fetal stroke volume (SV), left ventricular mass (LVM), and myocardial strain under different conditions. METHODS: To evaluate fetal hearts, fetal-sized rabbit hearts were used in this study. The in vitro portion of this study was carried out using a balloon inserted into the LV of eight fresh rabbit hearts and driven by a calibrated pulsatile pump. RT3DE volumes were obtained at various pump-set SVs. The in vivo experiments in this study were performed on open-chest rabbits. RT3DE volumes were acquired at the following conditions: baseline, simulated hypervolemia, inferior vena cava (IVC) ligation, and ascending aorta (AAO) ligation. Displacement values and sonomicrometry data were used as references for RT3DE-derived SV, LVM, longitudinal strain (LS), and circumferential strain (CS). RESULTS: Excellent correlations between RT3DE-derived values and reference values were demonstrated and accompanied by high coefficients of determination (R(2) ) for both in vitro and in vivo studies for SV, LVM, LS, and CS (in vitro: SV: R(2)  = 0.98; LVM: R(2)  = 0.97; LS: R(2)  = 0.87, CS: R(2)  = 0.80; in vivo: SV: R(2)  = 0.92; LVM: R(2)  = 0.98; LS: in vivo: R(2)  = 0.84; CS: in vivo: R(2)  = 0.76; all P < 0.05). CONCLUSIONS: RT3DE is capable of quantifying the SV, LVM, and myocardial strain of fetal-sized hearts under different conditions. This nongated RT3DE may aid the evaluation of fetal cardiac function, providing a superior understanding of the progress of fetal heart disorders.

Data Valuation with Gradient Similarity.

High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks.

Graph Structured Neural Networks for Perturbation Biology.

Computational modeling of perturbation biology identifies relationships between molecular elements and cellular response, and an accurate understanding of these systems will support the full realization of precision medicine. Traditional deep learning, while often accurate in predicting response, is unlikely to capture the true sequence of involved molecular interactions. Our work is motivated by two assumptions: 1) Methods that encourage mechanistic prediction logic are likely to be more trustworthy, and 2) problem-specific algorithms are likely to outperform generic algorithms. We present an alternative to Graph Neural Networks (GNNs) termed Graph Structured Neural Networks (GSNN), which uses cell signaling knowledge, encoded as a graph data structure, to add inductive biases to deep learning. We apply our method to perturbation biology using the LINCS L1000 dataset and literature-curated molecular interactions. We demonstrate that GSNNs outperform baseline algorithms in several prediction tasks, including 1) perturbed expression, 2) cell viability of drug combinations, and 3) disease-specific drug prioritization. We also present a method called GSNNExplainer to explain GSNN predictions in a biologically interpretable form. This work has broad application in basic biological research and pre-clincal drug repurposing. Further refinement of these methods may produce trustworthy models of drug response suitable for use as clinical decision aids. Availability and implementation: Our implementation of the GSNN method is available at https://github.com/nathanieljevans/GSNN. All data used in this work is publicly available.

Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction. MATERIALS AND METHODS: We developed and evaluated multimodal deep learning models for multiclass glaucoma trabeculectomy surgery outcomes using both structured EHR data and free-text operative notes. We compare those to baseline models that use structured EHR data exclusively, or neural network models that leverage only operative notes. RESULTS: The multimodal neural network had the highest performance with a macro AUROC of 0.750 and F1 score of 0.583. It outperformed the baseline machine learning model with structured EHR data alone (macro AUROC of 0.712 and F1 score of 0.486). Additionally, the multimodal model achieved the highest recall (0.692) for hypotony surgical failure, while the surgical success group had the highest precision (0.884) and F1 score (0.775). DISCUSSION: This study shows that operative notes are an important source of predictive information. The multimodal predictive model combining perioperative notes and structured pre- and post-op EHR data outperformed other models. Multiclass surgical outcome prediction can provide valuable insights for clinical decision-making. CONCLUSIONS: Our results show the potential of deep learning models to enhance clinical decision-making for postoperative management. They can be applied to other specialties to improve surgical outcome predictions.

Effects of Different Na+ Concentrations on cAMP-Dependent Protein Kinase Activity in Postmortem Meat.

cAMP-dependent protein kinase (PKA) activity regulates protein phosphorylation, with Na+ playing a crucial role in PKA activity. The aim of this study was to investigate the effects of different Na+ concentrations on PKA activity and protein phosphorylation level in postmortem muscle. The study consisted of two experiments: (1) NaCl of 0, 20, 100, 200 and 400 mM was added to a muscle homogenate incubation model to analyze the effect of Na+ concentration on PKA activity, and (2) the same concentrations were added to pure PKA in vitro incubation models at 4 °C to verify the effect of Na+ on PKA activity. The PKA activity of the muscle homogenate model increased with storage time in groups with different Na+ concentrations. High concentrations of Na+ inhibited sarcoplasmic protein phosphorylation. The PKA activity at 24 h of storage and the sarcoplasmic protein phosphorylation level at 12 h of storage in the group with 200 mM Na+ was lower than that of the other groups. After 1 h incubation, the PKA activity of samples in the 200 mM Na+ group was inhibited and lower than that in the other Na+ groups in the in vitro incubation model. These results suggest that the Na+ concentration at 200 mM could better inhibit PKA activity. This study provided valuable insights for enhancing curing efficiency and improving meat quality.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

Electron microscopy (EM) enables imaging at nanometer resolution and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task; however, analyzing them is now the bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, semi-supervised learning as well as next steps for the mitigation of the manual segmentation bottleneck.

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease.

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations.

Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light on how cancer evolves to develop resistance to therapy. Acquiring these images has become a routine task.However, analyzing them is now a bottleneck, as manual structure identification is very time-consuming and can take up to several months for a single sample. Deep learning approaches offer a suitable solution to speed up the analysis. In this work, we present a study of several state-of-the-art deep learning models for the task of segmenting nuclei and nucleoli in volumes from tumor biopsies. We compared previous results obtained with the ResUNet architecture to the more recent UNet++, FracTALResNet, SenFormer, and CEECNet models. In addition, we explored the utilization of unlabeled images through semi-supervised learning with Cross Pseudo Supervision. We have trained and evaluated all of the models on sparse manual labels from three fully annotated in-house datasets that we have made available on demand, demonstrating improvements in terms of 3D Dice score. From the analysis of these results, we drew conclusions on the relative gains of using more complex models, and semi-supervised learning as well as the next steps for the mitigation of the manual segmentation bottleneck.

Automated large volume sample preparation for vEM.

New developments in electron microscopy technology, improved efficiency of detectors, and artificial intelligence applications for data analysis over the past decade have increased the use of volume electron microscopy (vEM) in the life sciences field. Moreover, sample preparation methods are continuously being modified by investigators to improve final sample quality, increase electron density, combine imaging technologies, and minimize the introduction of artifacts into specimens under study. There are a variety of conventional bench protocols that a researcher can utilize, though most of these protocols require several days. In this work, we describe the utilization of an automated specimen processor, the mPrep™ ASP-2000™, to prepare samples for vEM that are compatible with focused ion beam scanning electron microscopy (FIB-SEM), serial block face scanning electron microscopy (SBF-SEM), and array tomography (AT). The protocols described here aimed for methods that are completed in a much shorter period of time while minimizing the exposure of the operator to hazardous and toxic chemicals and improving the reproducibility of the specimens' heavy metal staining, all without compromising the quality of the data acquired using backscattered electrons during SEM imaging. As a control, we have included a widely used sample bench protocol and have utilized it as a comparator for image quality analysis, both qualitatively and using image quality analysis metrics.

Deep Convolutional Neural Networks Detect no Morphological Differences Between Culture-Positive and Culture-Negative Infectious Keratitis Images.

Purpose: To determine whether convolutional neural networks can detect morphological differences between images of microbiologically positive and negative corneal ulcers. Methods: A cross-sectional comparison of prospectively collected data consisting of bacterial and fungal cultures and smears from eyes with acute infectious keratitis at Aravind Eye Hospital. Two convolutional neural network architectures (DenseNet and MobileNet) were trained using images obtained from handheld cameras collected from culture-positive and negative images and smear-positive and -negative images. Each architecture was trained on two image sets: (1) one with labels assigned using only culture results and (2) one using culture and smear results. The outcome measure was area under the receiver operating characteristic curve for predicting whether an ulcer would be microbiologically positive or negative. Results: There were 1970 images from 886 patients were included. None of the models were better than random chance at predicting positive microbiologic results (area under the receiver operating characteristic curve ranged from 0.49 to 0.56; all confidence intervals included 0.5). Conclusions: These two state-of-the-art deep convolutional neural network architectures could not reliably predict whether a corneal ulcer would be microbiologically positive or negative based on clinical photographs. This absence of detectable morphological differences informs the future development of computer vision models trained to predict the causative agent in infectious keratitis using corneal photography. Translational Relevance: These deep learning models were not able to identify morphological differences between microbiologically positive and negative corneal ulcers. This finding suggests that similar artificial intelligence models trained to identify the causative pathogen using only microbiologically positive cases may have potential to generalize well, including to cases with falsely negative microbiologic testing.

Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation.

Proper diagnosis of ADHD is costly, requiring in-depth evaluation via interview, multi-informant and observational assessment, and scrutiny of possible other conditions. The increasing availability of data may allow the development of machine-learning algorithms capable of accurate diagnostic predictions using low-cost measures to supplement human decision-making. We report on the performance of multiple classification methods used to predict a clinician-consensus ADHD diagnosis. Methods ranged from fairly simple (e.g., logistic regression) to more complex (e.g., random forest), while emphasizing a multi-stage Bayesian approach. Classifiers were evaluated in two large (N>1000), independent cohorts. The multi-stage Bayesian classifier provides an intuitive approach consistent with clinical workflows, and was able to predict expert consensus ADHD diagnosis with high accuracy (>86%)-though not significantly better than other methods. Results suggest that parent and teacher surveys are sufficient for high-confidence classifications in the vast majority of cases, while an important minority require additional evaluation for accurate diagnosis.

Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images.

Machine learning approaches have the potential for meaningful impact in the biomedical field. However, there are often challenges unique to biomedical data that prohibits the adoption of these innovations. For example, limited data, data volatility, and data shifts all compromise model robustness and generalizability. Without proper tuning and data management, deploying machine learning models in the presence of unaccounted for corruptions leads to reduced or misleading performance. This study explores techniques to enhance model generalizability through iterative adjustments. Specifically, we investigate a detection tasks using electron microscopy images and compare models trained with different normalization and augmentation techniques. We found that models trained with Group Normalization or texture data augmentation outperform other normalization techniques and classical data augmentation, enabling them to learn more generalized features. These improvements persist even when models are trained and tested on disjoint datasets acquired through diverse data acquisition protocols. Results hold true for transformerand convolution-based detection architectures. The experiments show an impressive 29% boost in average precision, indicating significant enhancements in the model's generalizibality. This underscores the models' capacity to effectively adapt to diverse datasets and demonstrates their increased resilience in real-world applications.

Assessing the Impact of Image Quality on Deep Learning Classification of Infectious Keratitis.

Objective: To investigate the impact of corneal photograph quality on convolutional neural network (CNN) predictions. Design: A CNN trained to classify bacterial and fungal keratitis was evaluated using photographs of ulcers labeled according to 5 corneal image quality parameters: eccentric gaze direction, abnormal eyelid position, over/under-exposure, inadequate focus, and malpositioned light reflection. Participants: All eligible subjects with culture and stain-proven bacterial and/or fungal ulcers presenting to Aravind Eye Hospital in Madurai, India, between January 1, 2021 and December 31, 2021. Methods: Convolutional neural network classification performance was compared for each quality parameter, and gradient class activation heatmaps were generated to visualize regions of highest influence on CNN predictions. Main Outcome Measures: Area under the receiver operating characteristic and precision recall curves were calculated to quantify model performance. Bootstrapped confidence intervals were used for statistical comparisons. Logistic loss was calculated to measure individual prediction accuracy. Results: Individual presence of either light reflection or eyelids obscuring the corneal surface was associated with significantly higher CNN performance. No other quality parameter significantly influenced CNN performance. Qualitative review of gradient class activation heatmaps generally revealed the infiltrate as having the highest diagnostic relevance. Conclusions: The CNN demonstrated expert-level performance regardless of image quality. Future studies may investigate use of smartphone cameras and image sets with greater variance in image quality to further explore the influence of these parameters on model performance. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Protein Phosphorylation Induced by Pyruvate Kinase M2 Inhibited Myofibrillar Protein Degradation in Post-Mortem Muscle.

Myofibrillar protein degradation is primarily related to meat tenderness through protein phosphorylation regulation. Pyruvate kinase M2 (PKM2), a glycolytic rate-limiting enzyme, is also regarded as a protein kinase to catalyze phosphorylation. The objective of this study was to investigate the relationship between myofibrillar protein degradation and phosphorylation induced by PKM2. Myofibrillar proteins were incubated with PKM2 at 4, 25, and 37 °C. The global phosphorylation level of myofibrillar proteins in the PKM2 group was significantly increased, but it was sensitive to temperature (P < 0.05). Compared with 4 and 25 °C, PKM2 significantly increased the myofibrillar protein phosphorylation level from 0.5 to 6 h at 37 °C (P < 0.05). In addition, the degradation of desmin and actin was inhibited after they were phosphorylated by PKM2 when incubated at 37 °C. These results demonstrate that phosphorylation of myofibrillar proteins catalyzed by PKM2 inhibited protein degradation and provided a possible pathway for meat tenderization through glycolytic enzyme regulation.

Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning.

Focused ion beam-scanning electron microscopy (FIB-SEM) images can provide a detailed view of the cellular ultrastructure of tumor cells. A deeper understanding of their organization and interactions can shed light on cancer mechanisms and progression. However, the bottleneck in the analysis is the delineation of the cellular structures to enable quantitative measurements and analysis. We mitigated this limitation using deep learning to segment cells and subcellular ultrastructure in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. The ultrastructures, such as nuclei, nucleoli, mitochondria, endosomes, and lysosomes, are relatively better defined than their surroundings and can be segmented with high accuracy using a neural network trained with sparse manual labels. Cell segmentation, on the other hand, is much more challenging due to the lack of clear boundaries separating cells in the tissue. We adopted a multi-pronged approach combining detection, boundary propagation, and tracking for cell segmentation. Specifically, a neural network was employed to detect the intracellular space; optical flow was used to propagate cell boundaries across the z-stack from the nearest ground truth image in order to facilitate the separation of individual cells; finally, the filopodium-like protrusions were tracked to the main cells by calculating the intersection over union measure for all regions detected in consecutive images along z-stack and connecting regions with maximum overlap. The proposed cell segmentation methodology resulted in an average Dice score of 0.93. For nuclei, nucleoli, and mitochondria, the segmentation achieved Dice scores of 0.99, 0.98, and 0.86, respectively. The segmentation of FIB-SEM images will enable interpretative rendering and provide quantitative image features to be associated with relevant clinical variables.

Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks.

Purpose: Develop computer vision models for image-based differentiation of bacterial and fungal corneal ulcers and compare their performance against human experts. Design: Cross-sectional comparison of diagnostic performance. Participants: Patients with acute, culture-proven bacterial or fungal keratitis from 4 centers in South India. Methods: Five convolutional neural networks (CNNs) were trained using images from handheld cameras collected from patients with culture-proven corneal ulcers in South India recruited as part of clinical trials conducted between 2006 and 2015. Their performance was evaluated on 2 hold-out test sets (1 single center and 1 multicenter) from South India. Twelve local expert cornea specialists performed remote interpretation of the images in the multicenter test set to enable direct comparison against CNN performance. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) individually and for each group collectively (i.e., CNN ensemble and human ensemble). Results: The best-performing CNN architecture was MobileNet, which attained an AUC of 0.86 on the single-center test set (other CNNs range, 0.68-0.84) and 0.83 on the multicenter test set (other CNNs range, 0.75-0.83). Expert human AUCs on the multicenter test set ranged from 0.42 to 0.79. The CNN ensemble achieved a statistically significantly higher AUC (0.84) than the human ensemble (0.76; P < 0.01). CNNs showed relatively higher accuracy for fungal (81%) versus bacterial (75%) ulcers, whereas humans showed relatively higher accuracy for bacterial (88%) versus fungal (56%) ulcers. An ensemble of the best-performing CNN and best-performing human achieved the highest AUC of 0.87, although this was not statistically significantly higher than the best CNN (0.83; P = 0.17) or best human (0.79; P = 0.09). Conclusions: Computer vision models achieved superhuman performance in identifying the underlying infectious cause of corneal ulcers compared with cornea specialists. The best-performing model, MobileNet, attained an AUC of 0.83 to 0.86 without any additional clinical or historical information. These findings suggest the potential for future implementation of these models to enable earlier directed antimicrobial therapy in the management of infectious keratitis, which may improve visual outcomes. Additional studies are ongoing to incorporate clinical history and expert opinion into predictive models.

Malignancy Detection in Prostate Multi-Parametric MR Images Using U-net with Attention.

Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.