Medical Imaging & EP Abnormality Detection

@schoudhary1553

As a full stack developer with an extensive background in data analysis and processing, I'm strongly convinced that my skill set would be highly advantageous for your project. Throughout my seven-year career, I've been entrusted with critical solutions, working with renowned companies such as Metlife GOSC and DXC Technologies. Specializing in Python, one of your preferred languages, I have perfected my craft in data scraping, crawling, and the usage of proven statistical analysis tools like SPSS. One particular aspect that resonates strongly between your project description and my expertise is the need to create a unified workflow that incorporates different data streams while providing clear and interpretable results. My experience will allow me not only to develop the pipeline but offer a clean and clinic-friendly interface for your utilization. Working towards 100% satisfaction, I guarantee fast turnaround time, fair costs, accuracy and promising all these without risks àas just paying well worth.

@nikhilgarg96

Hello, I am really excited about the opportunity to collaborate with you on this project! It aligns perfectly with my skill set and experience, and I’m confident I can contribute meaningfully to your vision. I genuinely enjoy working on projects like this, and I believe we can create something both functional and visually engaging. Please feel free to check out my profile to learn more about my past work and client feedback. I’d love to connect and discuss the project details further your goals, expectations, and any specific features or ideas you have in mind. The more I understand your vision, the better I can bring it to life. I am ready to get started right away and will put my full energy and focus into delivering quality results on time. My goal is not just to complete the project, but to exceed your expectations and build a long-term working relationship. Looking forward to hearing from you soon! With regards! Nikhil

@HiraMahmood4072

Hello, With over 7 years of experience in Data Processing and Data Science, I have carefully read through your project requirements for Medical Imaging & EP Abnormality Detection. To address your needs, I propose to create a unified workflow that integrates both imaging files and raw EP mapping exports. This workflow will involve data cleaning to remove clinical noise and feature extraction for further analysis. I will develop robust machine learning and deep learning models to detect arrhythmias and scar tissue with high sensitivity and provide clear explainability plots. Additionally, I will conduct side-by-side validation within each modality and provide a comparative report to showcase the agreement between the two data sources. The project will include lightweight documentation for seamless integration of new patient data. I am proficient in Python, PyTorch, and TensorFlow, and will ensure a comprehensive approach to meet your project objectives. Let's connect in chat to discuss further details and next steps. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@abhisaini0188

Hello, Thank you so much for posting this opportunity. It sounds like a great fit, and I’d love to be part of it! I’ve worked on similar projects before, and I’m confident I can bring real value to your project. I’m passionate about what I do and always aim to deliver work that’s not only high-quality but also makes things easier and smoother for my clients. Feel free to take a quick look at my profile to see some of the work I’ve done in the past. If it feels like a good match, I’d be happy to chat further about your project and how I can help bring it to life. I’m available to get started right away and will give this project my full attention from day one. Let’s connect and see how we can make this a success together! Looking forward to hearing from you soon. With Regards! Abhishek Saini

@Mahihassi

Having worked extensively in the domain of software engineering and IoT development, I am well-versed with not only the analytical and coding skills required for your project but also with the intricacies associated with data interpretation and feature extraction. My proficiency in Python, Linux, and circuit design can prove to be highly advantageous while handling both imaging files and raw EP mapping exports, as it ensures a holistic approach towards streamlining and structuring the data. Moreover, my strong command over Deep Learning, which is backed by a profound theoretical understanding of machine learning models and statistical tests, further strengthens my ability to develop robust algorithms that not only detect abnormalities with high sensitivity but do so in a manner that is clear and easy to explain. Something that sets me apart is my capability to provide lightweight documentation which essentially means that you would be provided with an accurate structured report of your patient cases without having to delve into the code. As you seek a ‘clean, clinician-friendly package’, I ensure delivering just that.

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@vorasiddh4it

Hello bandara, This project aligns perfectly with my hands-on work in medical imaging analytics and electrophysiology signal modeling. I’ve built multi-modal pipelines where imaging-derived features and EP maps are processed side-by-side with explainable ML/DL outputs. I can show demo notebooks and model code, then we finalize scope and terms. ? Unified Technical Workflow Python-based pipeline for imaging + raw EP exports Noise handling: Imaging: normalization, artifact suppression EP: baseline drift removal, temporal smoothing, spatial interpolation Feature engineering: Scar density, conduction velocity, activation dispersion Arrhythmia indicators from EP maps aligned to imaging markers ? Modeling & Explainability ML + DL (PyTorch / TensorFlow) CNNs / GNNs for spatial EP patterns Statistical validation + SHAP / saliency maps High-sensitivity detection of: Arrhythmias Scar tissue Conduction abnormalities (phase-ready) ? Validation & Reporting Cross-validation per modality Side-by-side agreement analysis (same patient cases) Clinician-friendly plots & metrics Plug-and-run design for new patients (no code edits) ? Relevant Projects Cardiac Imaging–EP Fusion Analytics Platform Arrhythmia Detection via Spatial EP Mapping Explainable AI for Clinical Signal & Image Data If translating imaging-grade insight into EP mapping excites you, let’s start. I’ll share a working prototype on de-identified data and iterate fast

@MZITSERVICES

Hi there, I am A.R.M. MASUD, with a strong Data Science background.I am an experienced Machine Learning developer with expertise in designing, training, and deploying intelligent models that deliver real-world value. My background includes supervised and unsupervised learning, deep learning with TensorFlow and PyTorch, and data preprocessing using Pandas, NumPy, and Scikit-learn. I specialize in developing classification, regression, clustering, and predictive models, as well as computer vision and NLP solutions. I follow best practices in feature engineering, hyperparameter tuning, and model evaluation to ensure high accuracy and scalability. My focus is building end-to-end ML pipelines that are efficient, reliable, and tailored to your project’s requirements for maximum impact. https://www.freelancer.com/u/MZITSERVICES I appreciate the opportunity to submit this proposal and am excited about the possibility of working with you to bring your project to life. Thanks A.R.M MASUD

@eliza019

Hello there, With extensive experience in statistical and predictive analytics, My proficiency in SPSS combined with my programming skills in Python will not only ensure an effective data analysis but also allow for flexibility and automation, optimizing efficiency throughout the process. Throughout my career, I have consistently proven my problem-solving mindset transforming complex datasets into clear and actionable insights that clients can use to drive their business forward. Data cleaning and transformation is an often-overlooked area of importance; however, it's where precise analysis begins. My vast experience in this area ensures thorough sorting of your data for accurate analysis - leaving no stone unturned. Lastly, I recognize that clear communication is vital to successful project completion. I assure you of my commitment in communicating regularly and effectively throughout this project, leaving no room for ambiguities. Trusting me with this project will not just avail you of a qualified individual but also someone who is dedicated in leveraging their knowledge and experience for unparalleled client satisfaction.

@Baehaki0315

Hi, I’m excited to help enhance your abnormality-detection pipeline by bringing impactful insights from electrophysiology (EP) mapping. ✅ My Plan: - Develop a unified workflow that integrates both high-resolution imaging and EP mapping data. - Implement robust machine learning and deep learning models to accurately flag arrhythmias and scar tissue. - Ensure clear explainability plots for model outputs and perform cross-validation for comprehensive assessment of both data streams. - Provide lightweight documentation for easy data input without disrupting existing code. In a similar project, I successfully integrated diverse medical imaging data with machine learning models to enhance diagnostic accuracy. The result was a system that provided clear and validated insights for clinicians. With over 5 years of experience in AI Development and Data Science, I specialize in Python, TensorFlow, and data processing. Let’s discuss how we can take this project forward after reviewing the initial prototype. Thanks,

@divyam12345

Dear Sir/Madam, I’m confident I can help enhance your abnormality-detection pipeline by developing a unified workflow for both imaging and electrophysiology (EP) mapping. With experience in machine learning, deep learning, and statistical analysis, I can build robust models that flag arrhythmias and scar tissue, while providing clear explainability plots. Let’s connect in the chatbox to discuss the project further, including the budget and timeline. To know more about my experience, let's talk in a freelancer call, and I can share more details and sample works in the chatbox. I am ready to work with you, please connect in the chatbox for further discussions. Thank You. Dr. Divya.

@Joejoe777

Hi there, I just read all the requirements. However, I have carefully read your project description and submitted my bid after thorough research and preparation. This is my proposal for project: I propose developing a unified workflow that integrates imaging files and raw EP mapping exports, cleans clinical noise, and generates structured features for analysis. Option A: Utilize machine learning and deep learning models to accurately identify arrhythmias and scar tissue, providing clear explainability plots for clinician understanding. Option B: Enhance feature engineering and expand to include broader ECG segments for a comprehensive analysis. Let's discuss in detail through chat.

@HNamdar

Hello, I will develop a unified, clinician-friendly machine learning pipeline to detect arrhythmias and scar tissue from electrophysiology (EP) maps with the same clarity as your current imaging-based detection. You’ll receive: 1. A Unified Data Ingestion & Processing Workflow – a clean Python pipeline to ingest, clean, and structure features from both EP mapping exports and imaging files for analysis. 2. High-Sensitivity Detection Models with Explainability – robust ML/DL models (PyTorch/TensorFlow) to flag arrhythmias and scar tissue, supported by explainability plots and statistical validation. 3. A Side-by-Side Validation Report – including cross-validation per modality and a comparative analysis showing agreement between imaging and EP data, plus lightweight documentation for seamless integration of new patient data. https://www.freelancer.pk/projects/data-analysis/Intro-Python-Data-Analysis-Mini/reviews https://www.freelancer.pk/projects/data-analysis/School-Data-Analysis-Projects/reviews Ready to start with a prototype on a de-identified dataset. Let's discuss timelines and next steps for feature engineering and conduction-delay exploration. Hassaan Namdar PhD Candidate (PT) | MSc (UK) Certified: Wharton & UC Berkeley

@maryam951

Hello bandara65, I am Maryam Abbas, a Python expert with 4 years of experience. I have carefully reviewed your project requirements for Medical Imaging & EP Abnormality Detection. I propose to create a unified workflow that processes imaging files and EP mapping exports, develops robust models for arrhythmia and scar tissue detection, and provides side-by-side validation for accurate results. I will ensure clear explainability plots and lightweight documentation for seamless integration of new patient data. With a proven track record in Python, PyTorch, and TensorFlow, I am confident in delivering a high-quality solution. Please review my portfolio https://www.freelancer.pk/u/maryam951 and let's discuss further details. Best regards, Maryam Abbas

@ahmaddar2970

Hi there, I’m excited to translate your imaging-expert pipeline to EP mapping and deliver a clinician-friendly, plug-and-play prototype. I’ll build a unified workflow that ingests high-resolution images and raw EP exports, cleans clinical noise, and outputs structured features for downstream analysis. Robust models (ML/DL with supporting statistical tests) will flag arrhythmias and scar tissue with high sensitivity and provide clear explainability plots. Approach: - Ingestion and cleaning: modular parsers for imaging and EP data, noise suppression, alignment, and standardization. - Modeling: modality-specific models with cross-validation inside each stream and a side-by-side agreement report across modalities. - Explainability: attribution maps and intuitive visuals to help clinicians interpret outputs. - Documentation: lightweight docs and notebooks to plug in new patient data without touching code. Deliverables: a working prototype on a small, de-identified dataset; Python notebooks (PyTorch or TF); evaluation report; simple docs. Timeline: ~14 days for the initial prototype, with quick iterations on features afterward. Next steps: confirm data formats, labeling conventions, and evaluation preferences What cross-modality evaluation metric do you prefer to assess agreement between imaging and EP detections (e.g., per-patient sensitivity, Cohen's kappa, or lesion-level metrics)? Best regards,

@dushyant317

Hello, I’ve carefully reviewed your project requirements and clearly understand the tasks involved. I have 13 years of experience and strong expertise in the exact skills this project requires. I have successfully delivered similar projects before and can share relevant samples if needed. I will complete this within your expected timeline while maintaining quality and clear communication. I look forward to working with you and contributing sincerely to your project’s success.

@Ravan011patel

Hello , FIRST PLEASE CHECK MY WORK EXPERIENCE THEN TRUST THE RESULT. I’ve carefully understood your requirement — extending an abnormality-detection pipeline across imaging and electrophysiology (EP) mapping, with strong focus on clinical reliability, explainability, and clean workflows. This is exactly the type of applied ML/AI work I specialize in. ✅ How I’ll Approach This • Build a unified ingestion & preprocessing pipeline for imaging + raw EP mapping data • Noise cleaning & feature extraction aligned with clinical signal characteristics • Develop ML/DL models (PyTorch / TensorFlow) to detect arrhythmias & scar tissue • Add explainability tools (saliency, SHAP, feature importance, activation maps) • Perform side-by-side validation & cross-modality comparison reports ✅ What You’ll Get • Structured, reusable feature outputs • Clinician-friendly visualizations & validation plots • Clear notebooks + lightweight documentation for adding new patient data • Prototype on de-identified dataset, then iterative refinement ✅ Why Me • Strong background in medical imaging, signal processing & applied AI • Focus on explainable, production-ready models, not black boxes • Clean, well-documented Python pipelines I believe in delivering robust, interpretable results that clinicians can trust. Let’s move from prototype to insight

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@SolutionMart

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