Bholendra Pratap Singh’s Post

🧠 News in #medicalimaging! 🧠 Readout from MICCAI BraTS MRI Synthesis Challenge (BraSyn)   💡 I am thrilled to share #TeamBayer #Radiology latest paper on the #MICCAI #BraSyn 2023 challenge: "Missing MRI synthesis and the effect of different learning objectives" by my esteemed colleagues Ivo Matteo Baltruschat Parvaneh Janbakhshi and Matthias Lenga. Kudos!   ❗ What is important: The critical issue of synthesizing missing MRI sequences to facilitate, e.g., tumor segmentation pipelines is addressed by this excellent work. The results demonstrating the significant influence of different loss functions on synthesis quality, ultimately optimizing image synthesis performance.   ‼️Why is this important? Automatic tumor segmentation in brain MRI is crucial for accurate diagnosis and monitoring. However, challenges arise when certain MRI sequences are missing due to time constraints or motion artifacts. Our work aims to bridge this gap by investigating the effectiveness of deep learning image-to-image translation approaches, providing a comprehensive comparison and benchmarking of different training procedures for brain MRI sequence synthesis.   ✅ Key Findings: Our paper comprises a systematic analysis of the effects of various loss functions on the quality of synthetically generated Brain MRIs. By combining different learning objectives into a multi-objective loss, we demonstrate the possibility of further improving image synthesis performance.   💡 Our findings are a step towards a more comprehensive understanding of the contributions of each proposed architecture variants, decoupled from the effects of different training objectives.   🚀 Join the Discussion! We invite you to read our full paper https://lnkd.in/gRzJaFYM and share your thoughts. Let's continue to drive innovation and progress in medical imaging together!    👉 Visit the MICCAI BraTS MRI Synthesis Challenge homepage: https://lnkd.in/gic-54XU   👍 Big thank you to the organizers:   Hongwei Bran Li Machine Learning Researcher for Ultra-low-field MR Image Analysis   Juan Eugenio Iglesias Medical Imaging researcher at MGH / Harvard Medical School   Spyridon Bakas Associate Professor & Director of Computational Pathology at Indiana University   #SeeThePossibleCreateTheFuture #BayerInRadiology #DeepLearning #HealthcareInnovation

🌟 Exciting News! 🌟 I am thrilled to announce the publication of my latest research paper in IEEE! The paper, titled "Optimizing Deep Learning Based Approach for Brain Tumor Segmentation in Magnetic Resonance Imaging (MRI) Scans," delves into the development and optimization of a deep learning framework to enhance the accuracy and efficiency of brain tumor segmentation in MRI scans. This work represents a significant step forward in medical imaging and has the potential to improve diagnostic processes and treatment planning for brain tumor patients. In this study, we explore various optimization techniques to refine the performance of our deep learning model, ensuring it can effectively identify and segment brain tumors with high precision. By leveraging advanced neural network architectures and comprehensive training datasets, our approach aims to provide reliable and swift analysis, which is crucial for early detection and intervention. I am incredibly proud to share this work with the academic and professional community, and I look forward to the positive impact it may have in the field of medical imaging. Thank you Dr. Mahesh T R Sir for your guidance and thanks to my co-authors🥰 Read the full paper here: https://lnkd.in/gXBBKKmP #mdalmahedihassan #Research #BrainTumorSegmentation #Researchpaper #Conferencepaper #IEEE #Innovation #Technology #MedicalImaging #DeepLearning #BrainTumor #MRI #NewPublication #AcademicResearch

🚀 Exciting New Research Publication !✨ It gives me immense pleasure to announce that our manuscript titled "Efficient Brain Tumor Classification using Filter-Based Deep Feature Selection Methodology", has officially been published in the journal "SN Computer Science"! This paper introduces a novel, lightweight two-stage framework for classifying brain tumors from structural MRI scans. 🔰 Key Highlights: Stage 1: Use of a pre-trained CNN for efficient feature extraction from pre-processed MRI, reducing training time and hardware requirements. Stage 2: A filter-based deep feature selection method to minimize computational load and prevent overfitting. 🔬 Results: Our method increased overall performance metrics by 6% on standard, benchmarked datasets while reducing the feature space by more than 25%. This work brings us closer to deploying AI-driven diagnostics for brain tumor detection in clinical settings, where speed, accuracy, and resource efficiency are essential. I am overwhelmed by the sheer dedication and perseverance of my talented co-authors Utathya Aich, and our supervisor Dr. PAWAN KUMAR SINGH, without whom this work wouldn't have been possible. 🔗 Check out our paper at this link: https://lnkd.in/dEbQxGR3 Code: https://lnkd.in/dWAs6Zbr #Research #AI #MedicalImaging #MRIScan #DeepLearning #BrainTumorClassification

🚀 New Research Alert! 🚀 I’m thrilled to share my latest research contribution in the field of Computer Vision and Medical Imaging. 📊🔬 📄 Title: Transforming Brain Tumor Diagnosis: Vision Transformers Combined with Ensemble Techniques 📚 Published in: Journal of Population Therapeutics and Clinical Pharmacology In this study, we explored advanced vision transformers and ensemble techniques to improve the accuracy and efficiency of brain tumor diagnosis. This work aims to push the boundaries of automated medical diagnostics, potentially benefiting healthcare professionals and patients alike. A special thanks to my collaborators and mentors for their support throughout this journey! 🙏 🧠 Keywords: AI, Deep Learning, Computer Vision, Medical Imaging, Brain Tumor Detection #Research #AI #DeepLearning #MedicalImaging #ComputerVision Raja Anees

Hello connections !! I am honored to have presented my research paper titled Comparative Analysis of CNN Architectures with Transfer Learning for Brain Tumor Detection and Classification from MRI Scans at the prestigious ICON BCIHT Conference organized by CDAC - Thiruvananthapuram! 🎉 This work focuses on leveraging the power of transfer learning and advanced CNN architectures to improve the accuracy and efficiency of brain tumor detection and classification using MRI scans. The findings highlight the potential of AI-driven solutions in revolutionizing healthcare diagnostics. 💻 Presenting at such a renowned platform was an incredible experience, and I am eager to continue contributing to advancements in the intersection of AI and healthcare. Here's to more research, innovation, and collaboration! #Research #AI #Healthcare #BrainTumorDetection #TransferLearning #DeepLearning #ICONBCIHT

🧠💻 AI Meets Neuroimaging: A Comprehensive Survey Excited to share: Our team's latest paper is out! "Deep learning for the harmonization of structural MRI scans: a survey" Published in BioMedical Engineering OnLine (Springer) This survey explores how AI is revolutionizing structural MRI analysis. Congrats to Soolmaz and the entire team, particularly, Gaurav Pandey, Nasim Sheikh- Bahaei and Jeiran Choupan for their guidance. #NeuroimagingAI #DeepLearning #MedicalResearch https://lnkd.in/g6TX37u5 What's your take on AI's role in advancing brain research?

🚀 Project Update! I’m excited to share progress on our DL-Based Detection and Documentation in MRI Scans project! 🔍 Overview: We’re leveraging AI to detect brain tumors and generate detailed medical reports from MRI scans. 🛠️ Technologies: •CNN & U-NET: For tumor detection and segmentation •PyRadiomics: For feature extraction •Large Language Models (LLM): For report generation 👥 Impact: •Surgeons: Enhanced diagnostic accuracy •Radiologists: Faster report generation •Patients: Better understanding of their medical conditions 🌟 Next Steps: •Expand to other tumor types •Integrate images into reports Excited for what’s next! Feedback is welcome. #Bioinformatics #AI #MedicalImaging #DeepLearning #Healthcare

I am thrilled to announce the publication of my paper titled "Segmentation of MR Images for Brain Tumor Detection Using Autoencoder Neural Network." In the realm of medical imaging, accurate segmentation of MR images is crucial for effective diagnosis and treatment planning. This study presents an innovative model designed to segment and identify local tumor formations in brain MR images. ### Abstract: Medical images often require segmenting into different regions in the initial analysis stage. Relevant features are selected to differentiate these regions, leading to meaningful anatomical segmentation. Our proposed system operates in an unsupervised manner to minimize expert intervention and expedite tumor classification. The methodology includes several preprocessing steps that enhance image normalization, resulting in improved accuracy and sensitivity in distinguishing tumors from healthy brain tissue. Utilizing a self-encoding neural network, we effectively reduce the dimensionality of tumor pixels, significantly aiding in the elimination of incorrectly identified tumor regions. By applying Otsu thresholding, we further extract the surrounding area and type of tumor. Our model was trained and tested using the BRATS2020 database and demonstrated remarkable performance metrics. The results indicate a 97% accuracy based on the Dice Similarity Coefficient (DSC), showcasing improved detection accuracy compared to existing methods and a reduction in diagnostic costs. I am grateful for the support from my colleagues and mentors throughout this journey. I look forward to your thoughts and feedback! #MedicalImaging #NeuralNetworks #BrainTumorDetection #ResearchPublication #Autoencoder #AIinHealthcare #MachinLearning #ImageProcessing

Disease Diagnosis with Image Classification in Machine Learning In healthcare, machine learning (ML) is transforming disease diagnosis through medical imaging. Image classification models, powered by deep learning, identify patterns and anomalies with remarkable accuracy, enabling early and precise diagnoses. 🔌 The Power of Image Classification Medical imaging is crucial in diagnosing diseases like cancer, cardiovascular disorders, and infections. ML models process large imaging data volumes efficiently, detecting subtle disease markers. 📌 Key Applications 1. Cancer: Detects tumors in mammograms, CT, and MRI scans, aiding early treatment. 2. Neurology: Identifies Alzheimer's, Parkinson's, and epilepsy through brain scans. 3. Infectious Diseases: Diagnoses TB, pneumonia, and malaria from medical images. 4. Retinal Health: Classifies images to detect diabetic retinopathy and glaucoma. 📌 Advantages - Accuracy & Speed: Rapidly reduces diagnostic errors. - Accessibility: Useful in underserved regions. - Scalability: Widely deployable for global impact. 📌 Challenges Addressing data biases, privacy, and transparency is critical. ML should augment, not replace, healthcare professionals. 📌 The Role of Biomedical Engineers Engineers bridge technology and medicine, developing ML models to integrate into clinical workflows, advancing diagnostics worldwide. The future lies in combining AI precision with human expertise to transform diagnostics. #MachineLearning #BiomedicalEngineering #AIinHealthcare

🧠🔍 Brain Tumor Image Classification with CNNs Excited to share my latest project where I utilized Convolutional Neural Networks (CNNs) to classify brain tumor images! 🌟 The Problem: Brain tumors are a critical health concern, and accurate diagnosis is essential for effective treatment. However, manual classification of brain tumor types from medical images can be time-consuming and prone to errors. The Solution: I leveraged the power of CNNs, a deep learning technique specifically designed for image analysis, to automate the classification process. By training the CNN model on a diverse dataset containing images of various brain tumor types such as pituitary, glioma, meningioma, and more, the model learned to accurately classify tumors based on visual patterns and features. Key Steps: Data Preparation: Curated and preprocessed a dataset of brain tumor images. Model Architecture: Designed and trained a CNN model with multiple convolutional and pooling layers. Training and Validation: Split the dataset into training and validation sets, ensuring the model generalizes well. Evaluation: Evaluated the model's performance using metrics like accuracy, precision, recall, and F1-score. Results: The CNN model demonstrated impressive accuracy in classifying brain tumor images, providing a valuable tool for healthcare professionals in aiding diagnosis and treatment decisions. Looking forward to sharing more about the technical aspects and outcomes of this project! Feel free to connect or reach out for further discussions. #BrainTumorClassification #DeepLearning #MedicalImaging #CNNs #HealthTech