European Society of Digital and Integrative Pathology - ESDIP’s Post

Congratulations to Dr. Satya Prakash Yadav (Associate Professor #scsetbennett) for acceptance of the #research paper, “Explainable Artificial Intelligence (XAI) Model for Cancer Image Classification” for #publication in Computer Modeling in Engineering & Science, Tech Science Press. The use of Explainable Artificial Intelligence (XAI) models becomes increasingly important for making decisions in smart healthcare environments. It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms. These models could enhance interpretability and explainability in decision-making processes that rely on artificial intelligence. Nevertheless, the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images. This research presents an advanced investigation of XAI models to classify cancer images. It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications. In addition, this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques. The proposed model integrates several techniques: end-to-end explainable evaluation, rule-based explanation, and user-adaptive explanation. The proposed XAI reaches 97.72% accuracy, 90.72% precision, 93.72% recall, 96.72% F1-score, 9.55% FDR, 9.66% FOR, and 91.18% DOR. It will discuss the potential applications of the proposed XAI models in the smart healthcare environment. It will help ensure trust and accountability in AI-based decisions, essential for achieving a safe and reliable smart healthcare environment. #bennettuniversity #FacultyatBU #ExplainableArtificialIntelligence #healthcare #algorithms #cancer #classification #deeplearning

"Embarking on Day 1 of ELECTRONEXUS, our healthcare revolution journey through Computer Intelligence, we delved into the transformative power of AI in medical diagnostics. Dr. Prabira Kumar Sethy Associate Professor in ECE led an illuminating session, shedding light on how artificial intelligence, deep learning, and machine learning are revolutionizing cancer diagnosis. With an emphasis on integrating technology seamlessly with medical science, the session provided a comprehensive understanding of the potential for enhanced healthcare solutions. Prabira Sir insights showcased how these advanced technologies, including neural networks, are not just tools but crucial allies in the fight against cancer. From identifying different types of cancer to enabling early detection through intricate data analysis, the fusion of AI and medical expertise is opening new frontiers in patient care. As we navigate this dynamic landscape, it's clear that collaboration between technology and healthcare professionals is paramount. Each breakthrough brings us closer to a future where timely, accurate diagnoses are the norm, empowering both patients and practitioners alike. As we bid farewell to the workshop, our hearts are filled with a sense of purpose and possibility. Armed with newfound knowledge and a shared commitment to innovation, we leave inspired to continue the journey towards a future where every individual has access to timely and effective cancer care. Here the day 1 of ELECTRONEXUS was accomplished successfully with a good learning. Let's carry this momentum forward and continue to learn new technologies on day 2 of ELECTRONEXUS! See you all on Day 2 of ELECTRONEXUS."🌟 #HealthcareInnovation #AIRevolution #MedicalTechnology #cancerdiagnosis #siliconggv #ieeeggv #innovate #involve #innovate #Silicon #ggu #siliconsociety #electronics #Electronexus #workshop #machinelearning #neuralnet #artificalintelligence #deeplearning

Innovating Biomedical Research with GPT-4! At MedUni Vienna, our research team has unlocked a new potential for AI in biomedicine with the development of "SimulateGPT." This cutting-edge simulator leverages the power of GPT-4, known from ChatGPT, to significantly improve the accuracy of classifying important genes in cancer cells and predicting cancer patient prognosis. In the study, published in Computers in Biology and Medicine, they explored how stepwise simulations of biological and medical processes using GPT-4 could yield better results. This breakthrough is particularly exciting for scenarios like mouse experiments, sepsis treatment support, and predicting essential cancer genes. By configuring GPT-4 with structured inputs and detailed instructions, SimulateGPT achieved remarkable accuracy, outperforming traditional GPT-4 responses. This demonstrates that LLMs like GPT-4 can transform biomedical research by providing flexible and interpretable text-based simulations. Moving forward, they aim to integrate biological databases and mathematical modeling to further refine these AI-based simulators. This innovative approach promises to enhance the understanding of complex biological systems and drive future biomedical research. A big thank you to their incredible team at the Institute of Artificial Intelligence and the CeMM Research Center for Molecular Medicine for their dedication and hard work! I am also on the mission to continue to pushing the boundaries of what's possible with AI at midoc. Monitoring your wellness isn't a luxury—it's a survival necessity. With midoc, it's possible. Get midoc on iOS and Android here- https://midoc.ai/ #BiomedicalResearch #AI #GPT4 #MedUniVienna #SimulateGPT #CancerResearch #Innovation

Looking basic course in AI for biomedical imaging

I remember during my time at university, our professor showed us a demo of an AI program that could identify tumors in medical images faster than a radiologist. The class was stunned—was this the future of healthcare we were witnessing? Yet, despite the awe, questions remained: Can AI ever truly replace the intuition and experience of a human doctor?  From predictive analytics to robot-assisted surgeries, AI is being used to enhance efficiency and accuracy in medical processes. Major hospitals and research institutions are investing in AI technologies that can analyze vast amounts of medical data in seconds. These systems help doctors identify diseases earlier, personalize treatments based on genetic information, and improve the overall patient experience. What are your thoughts?  #AInowadays #AIinuniversity

🌟 Exciting News in Medical Technology! 🌟 I’m thrilled to share a project close to my heart that I've been working on: A Brain Tumor Detection System using advanced machine learning integrated with a user-friendly graphical user interface (GUI). This system is designed to assist medical professionals by providing quick and accurate diagnoses of brain tumors directly from MRI images. Project Highlights: 🧠 Targeted Diagnosis: Utilizes a Convolutional Neural Network (CNN) to classify MRI images into four categories: glioma, meningioma, no tumor, and pituitary tumor. 🖥️ Real-Time Interface: Developed with Tkinter, the GUI allows for easy image uploads and displays tumor classifications instantaneously. 💡 Medical Impact: Provides a tool for preliminary diagnoses, potentially accelerating patient care and treatment decision-making. This project not only demonstrates the integration of deep learning and practical application software but also underscores the potential of AI in enhancing healthcare diagnostics. hashtag #MachineLearning hashtag #ArtificialIntelligence hashtag #HealthcareInnovation hashtag #MedicalImaging hashtag #DeepLearning

Fraunhofer MEVIS has developed a groundbreaking multitasking approach that enables foundation models to be trained cost-efficiently and quickly with a minimum of data. This approach solves the problem of data scarcity in medical imaging and can ultimately save lives. Systems of artificial intelligence (AI) are revolutionizing medicine by detecting diseases earlier, improving therapies, and easing staff workloads. Fraunhofer MEVIS's foundation model analyzes tissue samples quickly, reliably, and resource-efficiently with a fraction of the usual training data. Through supervised pre-training and the image registration method HistokatFusion, automatically annotated training data are generated, and the training process significantly accelerated. Central to a reliable diagnosis and successful therapy are clear indicators, also known as biomarkers. Adaptive AI systems help identify these measurable parameters in pathological images. The foundation model learns general features and regularities from a broad collection of tissue section images during its basic training. In a second step, the learned concepts are applied to specific questions, significantly reducing the need for extensive data sets. 💡 Highlight:  Successful 2nd place in the international SemiCOL competition The pre-trained model and the code are freely accessible (https://lnkd.in/eusMwMRp), allowing experts to develop their own solutions. This innovation could reduce the workload in pathology and improve therapeutic outcomes. Partner institutions are RWTH Aachen University, Universität Regensburg and Hannover Medical School. Johannes Lotz Raphael Schäfer Till Nicke #ArtificialIntelligence #MedicalImaging #Biomarkers #ComputationalPathology #Pathology #AI Learn more here👇 https://s.fhg.de/JdYY https://lnkd.in/eTHVy_tX https://about.histo.app/

🌟 The Future of Pathology: AI in Digital Microscopic Imaging 🌟 Artificial Intelligence is revolutionizing numerous industries, and healthcare is no exception. One of the most promising applications is AI in digital microscopic imaging, where it is transforming how we analyze and interpret complex biological data. 🔬 Enhanced Precision: AI-driven algorithms can detect subtle patterns in microscopic images that may be difficult for the human eye to catch, leading to faster and more accurate diagnoses. ⏳ Improved Efficiency: Automated image analysis speeds up the process, allowing pathologists to focus on complex cases and provide timely results, crucial in disease management. 🌍 Scalability: With AI, the ability to analyze large volumes of images becomes feasible, helping in global research efforts, especially in resource-limited settings where specialists may not always be available. 🎯 Personalized Treatment: By integrating AI, we can dive deeper into the cellular level, identifying unique biomarkers and creating more targeted, personalized treatment plans. As the demand for precision medicine grows, the role of AI in digital microscopic imaging will continue to expand, making healthcare more accessible, efficient, and accurate. #AI #DigitalPathology #Microscopy #HealthcareInnovation #ArtificialIntelligence #PrecisionMedicine

This is an interesting case study/article that explores many of the possibilities that generative AI could have on the field of histological pathology. A field that has been heavily impacted by increasing medical technologies but has always relied on the physical eyes of a practicing pathologist to diagnose disease. It is shocking to learn that scientists are finding ways to digitize pathology slides and computers are able to develop biomarkers that ultimately lead to diagnosis. These AI computer models are so well trained at realizing trends and the practical applications that they may possess are almost limitless! https://lnkd.in/ekY2BSAx

🎉 Excited to share a milestone! 🧠✨ After months of dedicated work, I'm thrilled to announce the successful implementation of my thesis project, focusing on the segmentation of brain tumor sub-regions using a cutting-edge model combining CNN and vision transformer technologies. 🚀 Throughout this journey, I delved deep into the realms of artificial intelligence and medical imaging, aiming to enhance the accuracy and efficiency of brain tumor analysis. By leveraging Convolutional Neural Networks (CNNs) and harnessing the power of Vision Transformers, my model has exhibited remarkable performance in segmenting various sub-regions of brain tumors from MRI scans. The fusion of CNNs and vision transformers allows for a comprehensive analysis of intricate details within medical images, enabling precise identification and classification of different tumor components. This holistic approach not only aids in accurate diagnosis but also facilitates personalized treatment planning, ultimately improving patient outcomes. Moreover, this achievement underscores the pivotal role of artificial intelligence in revolutionizing healthcare. AI-driven solutions offer unprecedented opportunities to augment medical practices, empowering clinicians with advanced tools for diagnosis, prognosis, and treatment planning. By automating tedious tasks and providing insights from vast datasets, AI streamlines workflows and enhances the overall efficiency of healthcare systems. Furthermore, the integration of AI technologies in healthcare holds immense promise for early detection and intervention, potentially saving lives by identifying diseases at their nascent stages when they are most treatable. This paradigm shift towards AI-powered healthcare signifies a paradigmatic leap forward in improving patient care and advancing medical research. As I reflect on this journey, I'm deeply inspired by the transformative potential of AI in healthcare. I'm grateful for the opportunity to contribute to this evolving field and look forward to further innovations that will continue to redefine the landscape of healthcare delivery. Together, let's harness the power of AI to create a healthier and more equitable world. 💡 #AIinHealthcare #MedicalInnovation #ResearchMilestone #AIinHealthcare #MedicalAI #BrainTumorSegmentation #CNN #VisionTransformer #MedicalImaging #ResearchMilestone #BRATSChallenge #HealthTech #ArtificialIntelligence #HealthcareInnovation #PrecisionMedicine #DeepLearning #MRIAnalysis #DataScience #HealthcareTechnology #ThesisProject #InnovationInMedicine