Singularity University’s Post

"Exciting development in cardiac health and disease understanding! A new unsupervised neural network, Aladdin, is revolutionizing the analysis of left atrial motion and deformation using 3D Cine MRI. This innovation provides valuable insights into cardiovascular health and disease, with potential clinical applications. #AI #ML #HealthTech #Cardiology"

🚀 Did you know that practice doesn’t just make perfect, it crystallizes your brain? 🧠 Researchers at Rockefeller and UCLA found that repeating a task solidifies neural pathways, making performance automatic. Imagine if remembering where you parked was this easy! 😅 They used high-tech brain imaging to watch 73,000 neurons in action. Talk about data overload! 📊 This “crystallization” process shows why you can't get that catchy jingle out of your head after the 50th time. 🎶 Consistent messaging = brand recall gold! So next time you’re annoyed by repetitive ads, remember, they’re just helping your brain solidify those pathways! https://buff.ly/3UPZKFk #ScienceMagic #MarketingInsights #MemoryMastery #ToolsOfInfluence

A Year of Journal Articles (Day 195/365): Ning, Kaida, Pascale B. Cannon, Jiawei Yu, Srinesh Shenoi, Lu Wang, Alzheimer’s Disease Neuroimaging Initiative, and Joydeep Sarkar. "3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer’s disease sub-scores." Brain Informatics 11, no. 1 (2024): 5. Summary: - Different areas of cognitive function decline in Alzheimer's patients. - The team studied how brain imaging relates to specific cognitive decline. - The authors used 3D models to predict cognitive sub-scores based on MRI and FDG-PET scans. - Each cognitive sub-score linked to specific brain regions in each imaging modality. - MRI linked to structural changes in subcortical regions (e.g., hippocampus). - FDG-PET linked to metabolic changes in cortical regions (e.g., cingulated gyrus). - Findings suggest different imaging modalities capture different aspects of Alzheimer's. - Investigating multiple modalities may offer deeper insights into the disease.

📢 Read our Review paper 📚 HGG and LGG Brain Tumor Segmentation in Multi-Modal MRI Using Pretrained Convolutional #NeuralNetworks of Amazon Sagemaker 🔗 https://lnkd.in/gtXd5UD2 👨🔬 by Dr. Szidónia Lefkovits et al. #openaccess #MRI #deeplearning #CNN #AWSSagemaker

Manipulating Neuron Building Blocks Uncovers New Insights into Nerve Growth and Healing Dynamics https://lnkd.in/g2DYAcwR New Study Manipulates Neuron Building Blocks to Uncover Insights into Nerve Growth and Healing Dynamics, revealing the impact of posttranslational modifications on microtubules and their role in neural development and regeneration.

🚀📚 Thrilled to announce the publication of our latest research paper titled "Brain Tumor Detection: A Comprehensive Study Of Deep Learning And Machine Learning Techniques For MRI Analysis" in the esteemed International Journal of Creative Research Thoughts (IJCRT)! 📝🧠 Let's continue pushing the boundaries of innovation in healthcare. 💡 #Research #HealthcareInnovation #DeepLearning #MachineLearning #MRIAnalysis #BrainTumorDetection #IJCRTPublication

🚀 Excited to share our latest breakthrough in medical imaging! 🧠✨ Our article in co-authorship with Nisharg Nargund, "Revolutionizing Brain Tumor Detection: The CNN-Based Medical Imaging Breakthrough," has just been published in INFORMS MAGAZINE USA. Brain tumours pose significant challenges to healthcare; we have developed a cutting-edge Convolutional Neural Network (CNN)-based model for classifying MRI images. 🖥️🔍 This deep learning model achieves a remarkable individual accuracy rate of 99%, promising early and accurate detection of brain tumours. 🌐 Read the full article here:  https://lnkd.in/gyz_maNR The implications of this breakthrough are enormous—enabling prompt diagnosis, improved patient outcomes, and a positive impact on medical imaging analysis. Our commitment doesn't stop here; we're continuously refining the model, incorporating advanced techniques like data augmentation to enhance its performance further. Join us on the journey of transforming medical imaging and making a substantial difference in patients' lives! 🌟💙 #MedicalImaging #BrainTumorDetection #DeepLearning #Innovation #HealthcareTech #CNN #Research #informs

I'm excited to say that my research paper is going to be published and has been accepted for *oral* presentation at the prestigious MIDL Conference in Paris! The research demonstrates a new way of training deep convolutional neural networks to work with multiple diseases and differing MRI modalities. I want to thank my supervisor Konstantinos Kamnitsas and everyone in his lab that helped make this happen. Special thanks to Wentian Xu who assembled the research findings into a polished paper, and who will be presenting this July. The paper asks: "Can we train 1 multi-modal MRI segmenter for multiple types of brain 🧠 lesions?" - (the answer is yes!) We show a range of benefits, including improved performance from cross-disease learning, the ability to generalise with new modalities, and using the pre-trained models as a starting point for fine-tuning on new types of lesions. The research opens the door to a wide array of applications for multi-lesion multimodal MRI models and I'm excited to see upcoming research in this space. (I'm also pretty happy I managed to name an ML model architecture after myself - see MAFUNet 😀). Arxiv Paper: https://lnkd.in/gv2bxFzx Openreview: https://lnkd.in/gapWDfmB An article I wrote: https://lnkd.in/gXuDW3JX Code to be published soon.

📢 Excited to share that our paper titled "Aggregating Efficient Transformer and CNN Networks using Learnable Fuzzy Measure for Breast Tumor Malignancy Prediction in Ultrasound Images" has been published in the Neural Computing and Applications journal! 📚 🔗 You can access the paper here: [DOI: https://lnkd.in/eJ4yc-9K) In this work, we introduce an efficient method for predicting breast tumor malignancy (EPTM) in BUS images. EPTM leverages heterogeneous deep learning-based feature extraction models and a Choquet integral-based fusion mechanism. Specifically, it incorporates efficient CNN and Vision-Transformer methods to construct accurate Individual Breast Tumor Malignancy Prediction Models (IMMs). The heterogeneous IMMs ensure a comprehensive feature representation of breast tumors, as each CNN or Vision-Transformer captures different textural patterns in BUS images. EPTM employs a learnable fuzzy measure-based Choquet integral to fuse the predictions of IMMs, where the gray wolf optimizer is employed for generating fuzzy measures. This innovative approach holds promise for enhancing the accuracy and reliability of breast tumor malignancy predictions, contributing to the advancement of medical imaging and diagnostic capabilities. 🩺🔍 Let's continue pushing the boundaries of AI in healthcare! 💡👩⚕️👨⚕️ #MedicalImaging #AIinHealthcare #ResearchPaper #NeuralComputing #BreastCancerDetection

A Year of Journal Articles (Day 274/365): Hosseini, Mohammad Mehdi, Mohammad H. Mahoor, Jason W. Haas, Joseph R. Ferrantelli, Anne-Lise Dupuis, Jason O. Jaeger, and Deed E. Harrison. "Intra-Examiner Reliability and Validity of Sagittal Cervical Spine Mensuration Methods Using Deep Convolutional Neural Networks." Journal of Clinical Medicine 13, no. 9 (2024): 2573. Summary: Objective: Investigate the reliability of PostureRay® software compared to human experts in analyzing cervical spine alignment from lateral radiographs. Methods: Analyzed 254 lateral cervical radiographs of patients aged 11-86. Two blinded trials conducted by an expert user (US) with a 2-week interval. US clinician used PostureRay® to measure cervical spine angles, translations, and rotations. Reliability Assessment:Intra-class correlations (ICC), root mean squared error (RMSE), and R2 values were used. Results:PostureRay® showed perfect reliability (ICC=1.0, RMSE=0, R2=1) in repeated measurements. US clinician demonstrated excellent reliability (ICC ≥ 0.8 for most variables). Excellent construct validity (ICC ≥ 0.8) found between US and PostureRay® for most variables. Conclusion:PostureRay® shows reliability and strong agreement with expert human analysis for cervical spine measurements.