MedPulse AI’s Post

✨ Exciting News! ✨ I'm thrilled to share that one of our research articles titled "TransResUNet: Revolutionizing Glioma Brain Tumor Segmentation through Transformer-Enhanced Residual UNet" has been accepted for publication in IEEE Access, with an Impact Factor of 3.4. This work dives into the potential of Transformer-based architectures combined with Residual UNet for more accurate and efficient segmentation of glioma brain tumors, pushing the boundaries of AI in healthcare and contributing to the advancement of medical imaging. #AI #Healthcare #Research #IEEEAccess #BrainTumorSegmentation #MedicalImaging #Transformers #UNet #DeepLearning #AIinHealthcare #GliomaResearch

#ASNR2024 Are you keeping up with the latest in AI-driven for brain tumor segmentation? Join us and Dr. Charles Mellerio at the ASNR’s Industry Collaborative Session on "AI Applications in Brain Tumor Segmentation" and hear directly from neuroradiology experts and industry innovators on clinically translating AI advances to automate tumor volumetrics. Learn more about our tool and participate in a panel discussion. We hope to see you there as we discuss powering clinical decision-making with transformative segmentation technologies. 📆 2:55 pm Tuesday, May 21 📍 Emperors I - Caesars Palace - Las Vegas #AI #braintumor #medicalimaging Lauren G. Eloïse ZANETTI

AI Advancement in Urology Medical Care. The impact of #AI in detecting prostate cancer is significant and continues to evolve. AI technologies, such as machine learning algorithms, have shown promise in analyzing medical imaging data (such as MRI or ultrasound images) to assist radiologists and clinicians in identifying potential signs of prostate cancer at an early stage. #AugmentedIntelligence refers to the collaboration between human healthcare professionals and advanced technology like #AI. In the context of medicine, Augmented Intelligence can enhance decision-making processes by providing insights from vast amounts of patient data that may not be readily apparent through traditional methods alone. Harvey Castro, MD, MBA. discussion likely emphasizes how these advancements can lead to more accurate diagnoses, personalized treatment plans, improved patient outcomes, and overall efficiency within healthcare systems. Additionally, he might explore the ethical considerations surrounding the use of AI in medicine while highlighting its potential for transforming various aspects of clinical practice. https://lnkd.in/eqzxytCN

AI Advancement in Urology Medical Care. The impact of #AI in detecting prostate cancer is significant and continues to evolve. AI technologies, such as machine learning algorithms, have shown promise in analyzing medical imaging data (such as MRI or ultrasound images) to assist radiologists and clinicians in identifying potential signs of prostate cancer at an early stage. #AugmentedIntelligence refers to the collaboration between human healthcare professionals and advanced technology like #AI. In the context of medicine, Augmented Intelligence can enhance decision-making processes by providing insights from vast amounts of patient data that may not be readily apparent through traditional methods alone. Harvey Castro, MD, MBA. discussion likely emphasizes how these advancements can lead to more accurate diagnoses, personalized treatment plans, improved patient outcomes, and overall efficiency within healthcare systems. Additionally, he might explore the ethical considerations surrounding the use of AI in medicine while highlighting its potential for transforming various aspects of clinical practice. https://lnkd.in/eqzxytCN

What revolutionary AI applications are the most engaging to you right now? We're heartened to read about the continued growth of deep learning models that are trained to detect breast cancer. These models' ever-increasing accuracy and precision is very promising, especially in countries with limited resources for radiology training. Explore this powerful study on DL models for mammography in Nature ⏩ https://lnkd.in/gtXFb5rM #machinelearning #deeplearning #artificialintelligence #ai #aiforgood

I'm thrilled to share my first preprint on "Breast Cancer Detection using Mammography: Image Processing to Deep Learning." Explore our comprehensive review on the advancements in this crucial field. Your insights and feedback are highly valued. Read the full article here: https://lnkd.in/d6fukR7S. #BreastCancerResearch #Mammography #DeepLearning #MedicalImaging

Excited to share our group project on brain tumor classification! 🎓 We developed a model using PyTorch and CUDA to identify three types of brain tumors: glioma, meningioma, and pituitary by using CNN. Proud of the team's hard work and dedication! #MachineLearning #DeepLearning #BrainTumorClassification #PyTorch #CUDA #AI

All praise to Lord Shiva 🍁 I am excited to share our latest research on Explainable Ensemble Machine Learning! In our new paper, "An Explainable Ensemble Approach for Advanced Brain Tumor Classification Applying Dual-GAN Mechanism and Feature Extraction Techniques over Highly Imbalanced Data", we introduce a novel and reliable pipeline leveraging GANs and deep feature extraction methods to significantly enhance the accuracy of brain tumor classification from MRI images. Our approach effectively addresses challenges posed by highly imbalanced data and demonstrates strong generalizability, achieving superior accuracy compared to state-of-the-art deep learning models. These results underscore the potential of our proposed mechanism to improve computer-aided early-stage brain tumor diagnosis. Read our paper: https://lnkd.in/gXf2wsUC [ResearchGate] I am immensely grateful to the co-authors Fahim Mohammad Sadique Srijon and Pankaj Bhowmik Sir for their invaluable input & support. #AIML #plos #research #brainTumor #XAI #explainability

I’m refining a model for breast cancer detection using MRI. I’m separately evaluating DenseNet’s dense connectivity and ResNet’s residual learning to find the most effective architecture. I’m also considering Random Forest due to its success in tissue classification in some studies. What are your thoughts on the most effective architecture or methods for achieving the best diagnostic accuracy in this field? #CNN #DenseNet #ResNet #RandomForest #ResidualLearning #TransferLearning #MedicalImaging #AI #MachineLearning

🌟 Join Aiforia Technologies's upcoming webinar to discover how AI is revolutionizing the work of oncologists and pathologists in the #gastrointestinal field. The expert speakers will introduce an AI model that identifies key histological features of #colorectalcancer (CRC) and provides recurrence prediction estimates to aid in treatment decisions. Given the significant morphologic heterogeneity in CRC, accurate predictions can guide prognosis, uncover molecular alterations, and determine therapy responses. Learn about QuantCRC, a deep learning segmentation algorithm, that uses quantitative data from digitized H&E-stained slides on the Aiforia® Platform to improve tumor recurrence predictions. This AI model identifies clinically relevant prognostic risk groups, enhancing routine pathologic reporting of CRC. ➡️ Learn more + register here: https://buff.ly/4bLwndY Expert speakers: Thomas Westerling-Bui Rish Pai #Oncology #ColonCancer #Biomarkers #DigitalHealth #Pathology #MedicalImaging #ClinicalData #TherapeuticAreas #LaboratoryTechnology #DigitalPathology #