Retinopathy Detection & Progression Model

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@nrajuu07

I'll build a research-grade Temporal Lesion-Aware Dynamic Gated Multimodal Fusion framework for DR grading and DME detection using OLIVES and MMRDR datasets — combining fundus images, OCT scans, and clinical metadata through a dynamic gated cross-modal fusion mechanism, lightweight temporal transformer for progression tracking, and lesion-aware attention for clinically meaningful biomarker localization. Explainability outputs include Grad-CAM++, SHAP, and retinal heatmaps interpretable by ophthalmologists, with cross-dataset evaluation (OLIVES → MMRDR) to validate real-world generalization. Deliverables include fully reproducible code, trained weights, inference script, and a concise experiment report. Ready to start immediately.

@mohjib092

Hi there, I am python hub , Are you looking for a deep learning researcher to develop and implement your Temporal Lesion-Aware Dynamic Gated Multimodal Fusion framework for DR and DME analysis using the OLIVES and MMRDR datasets with multimodal fusion, temporal learning, and explainable AI integration?

@sudhir0112

Hi! I'm Sudhir Jain, a Machine Learning and Data Science specialist with hands-on expertise in Python, deep learning (PyTorch/TensorFlow), CNNs, and medical image analysis. This framework is highly sophisticated and I have the technical depth to implement it correctly. Here's my experience relevant to your project: - Deep Learning & CNNs: Proficient in implementing Swin Transformer, EfficientNet, and custom attention mechanisms using PyTorch/TensorFlow. - Multimodal Fusion: Experience building dynamic gated cross-modal fusion architectures combining heterogeneous inputs (images + metadata). - Temporal Models: Familiar with temporal transformers for time-series medical data. - Medical AI: Experience with image segmentation, classification, and biomarker detection tasks. - Explainability: Grad-CAM++, SHAP, and attention visualization for clinical interpretability. My implementation plan: 1. Set up OLIVES and MMRDR dataset pipelines 2. Implement modality-specific branches (Swin-T for fundus, EfficientNet-B3 for OCT, MLP for clinical) 3. Build the Dynamic Gated Cross-Modal Fusion module 4. Implement Lesion-Aware Attention and Temporal Transformer 5. Multi-task training: DR grading, DME detection, biomarker prediction, progression analysis 6. Explainability outputs: Grad-CAM++, SHAP, temporal attention heatmaps 7. Cross-dataset evaluation (OLIVES → MMRDR) I can start immediately. Shall we begin with the dataset setup and architecture verification?

@hassam945

Hi, I have already worked in this domain you can see my Freelancer portfolio project “AI System to Detect Diabetic Retinopathy,” where I used Swin Transformer-based retinal analysis with attention mechanisms and advanced feature fusion for DR detection. Your framework strongly aligns with my experience in multimodal retinal AI, explainable AI, and transformer-based medical systems. I understand the key challenge is building a robust temporal multimodal pipeline that learns relationships between Fundus, OCT, and clinical metadata while maintaining cross-dataset generalization and clinical interpretability. I can help implement temporal modeling, lesion-aware attention, dynamic gated multimodal fusion, explainability modules (Grad-CAM++/SHAP), and OLIVES/MMRDR evaluation workflows. My approach is always workflow-first: first finalize data flow, temporal strategy, fusion architecture, and evaluation protocol, then implement the framework step by step with clean, reproducible research-grade code. I can assure you a well-engineered and research-quality implementation. Best Regards

@adriank06

Building a Temporal Lesion-Aware Dynamic Gated Multimodal Fusion Framework requires precise handling of longitudinal medical data and complex cross-modal interactions. You need a robust architecture that seamlessly integrates fundus images, OCT scans, and clinical metadata to accurately classify DR/DME severity while tracking temporal progression across the OLIVES and MMRDR datasets. As an AI Engineer with over 4 years of specialized experience in Computer Vision and deep learning, I have successfully built multimodal medical imaging models and attention-based diagnostic systems using architectures like Swin Transformers and EfficientNet. Regarding the temporal transformer module, how many longitudinal visits on average are you sequencing per patient from the OLIVES dataset? Additionally, do you have a preferred gating mechanism for the cross-modal fusion, or should I propose a custom learnable weight approach to maximize the interpretability via Grad-CAM++ and SHAP? Shall we open a chat to review the sample files and evaluation protocols?

@PyMLExpert1

Leveraging my extensive experience in Deep Learning and Python programming, I am the ideal candidate for your Retinopathy Detection & Progression Model project. Your framework description resonates with not only my skill set but also my passion for problem-solving within the medical domain. I have worked on similar projects that demanded complex algorithm development and integration of multimodal medical data to produce precise predictions. Specifically, my domain expertise includes designing intelligent systems with Dynamic Gated Multimodal Fusion mechanisms. This aligns perfectly with your requirements of adaptively learning the importance of each retinal modality, thereby enhancing both predictive accuracy and explainability. In addition, I am adept at handling large-scale datasets like OLIVES and MMRDR, critical for successful inter-dataset training and evaluation. Moreover, I understand the significance of temporal disease progression analysis in DR and DME detection. With a Lightweight Temporal Transformer module in place, I can seamlessly model retinal progression patterns essential for accurate classification, biomarker prediction, and temporal progression analysis as well as Experience with Grad-CAM++ and SHAP will empower me in generating the clinically interpretable outputs you seek. Lastly, my track record speaks volumes about my ability to ensure cross-dataset robustness which is a critical aspect of your project.

@sgodeveloper

As a seasoned professional in web and mobile application development with a strong emphasis on performance and usability, I'm uniquely equipped to execute your Retinopathy Detection & Progression model. My 3+ years of practical experience have been distilled largely in the realm of Machine Learning (ML), and Python - just the skills needed for effective execution of your project. Expect nothing less than clear communication, quick delivery, long-term support and product that truly scales from this partnership. I've dealt with complex systems before and your project presents another exciting opportunity for me to leverage my skills to meet client needs while finding scalable alternatives. The proposed Retinopathy Detection framework you’ve outlined aligns superbly with my skill set and previous work showcases similar applications successfully delivered. Building a strong relationship is vital for success. Let's connect to discuss further in achieving your specific objectives with this project while I await sample files for an enhanced clarity on data format or evaluation protocol if need be

@jamesdfisher18

Hi, I can implement this Temporal Lesion-Aware Dynamic Gated Multimodal Fusion framework for DR/DME analysis using OLIVES and MMRDR. I’ll build a PyTorch pipeline with separate fundus, OCT, and clinical metadata branches using Swin-Tiny, EfficientNet-B3, and an MLP encoder, then add temporal transformer modeling, lesion-aware attention, and dynamic gated cross-modal fusion for adaptive multimodal prediction. The system can support DR grading, DME detection, biomarker prediction, temporal progression analysis, and cross-dataset evaluation such as OLIVES → MMRDR. I’ll also include Grad-CAM++/SHAP-based explainability, attention visualizations, retinal heatmaps, and clear evaluation metrics. Deliverables will include clean training/testing code, preprocessing, model architecture, results logs, and documentation so the work is reproducible and research-ready.

@beshoya32

Hi, I’m very interested in your multimodal DR/DME analysis project. I recently worked on an AI-based retinal disease detection system focused on Cataract, Glaucoma, and Diabetic Retinopathy classification using deep learning and fundus imaging, which makes your project highly aligned with my experience. I have hands-on experience with: * Python, PyTorch, TensorFlow * CNNs, Transformers, and medical image classification * Retinal image preprocessing and evaluation * Explainability techniques such as Grad-CAM * Data analysis and model experimentation Your proposed framework involving Swin Transformer, EfficientNet, temporal learning, and multimodal fusion is very interesting, and I would be excited to contribute to its implementation and experimentation. I’m also comfortable working with research-oriented pipelines, dataset preprocessing, training workflows, and evaluation metrics for medical imaging tasks. I’d be happy to discuss the project details further. Best regards, Beshoy Arnest

@Amirhosseinas

Hello, I am a Python Developer and Machine Learning Engineer with strong experience in building data-driven applications, automation tools, and AI solutions. My background includes Python, SQL, Linux, FastAPI, and working with image-processing and machine learning workflows. I can develop a complete Retinopathy Detection and Progression Model using modern deep learning frameworks such as TensorFlow/Keras or PyTorch. The solution will include: - Data preprocessing and augmentation for retinal fundus images - Transfer learning using architectures such as EfficientNet, ResNet, or DenseNet - Model training, validation, and hyperparameter tuning - Performance evaluation using Accuracy, Precision, Recall, F1-Score, and ROC-AUC - Grad-CAM visualizations for model interpretability - Disease progression prediction (if longitudinal patient data is available) - Well-documented code and Jupyter notebooks - Optional API deployment with FastAPI I focus on building accurate, reproducible, and well-documented solutions while maintaining clear communication throughout the project. Although I am new to Freelancer, I have real-world professional experience and a strong commitment to delivering high-quality results on time. I am available to start immediately and would be happy to discuss your dataset, performance goals, and preferred framework. Best regards, Amirhossein Nouralizad