AI HER2 Slide Diagnosis Software

@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

@kamran2012

Hello, I will build your Windows desktop application for HER2-neu slide analysis — WSI stitching via grid/mosaic, model training on your labeled cases, and an inference panel showing AI diagnosis with confidence scores alongside human results, plus CSV/Excel export for your thesis statistics. For the model, I will use a patch-based classification approach with attention pooling — this handles large WSI files without requiring massive GPU memory, and the attention weights naturally produce heat-maps over diagnostically relevant regions at no extra complexity. Questions: 1) How many labeled HER2-positive slides do you currently have available for training? 2) What GPU does your machine have — this will determine batch size and whether we need mixed-precision training? Looking forward to potentially working together. Thanks, Kamran

@vishal1145

Your HER2 slide analysis will fail if the model can't handle gigapixel WSI files - most frameworks crash above 2GB without proper tiling and memory management. I've built similar diagnostic tools for pathology labs where a single inference error cost weeks of re-validation. Before architecting this, I need clarity on two things: What's your average WSI file size and resolution? Are you expecting real-time inference (under 30 seconds per slide) or can processing take 5-10 minutes? This determines whether we use a lightweight CNN or a transformer-based model with GPU acceleration. Here's the technical approach: - PYTHON + PYTORCH: Build a patch-based training pipeline that tiles WSIs into 256x256 regions, trains on HER2-positive samples using transfer learning from ImageNet weights, then reconstructs full-slide predictions with confidence heatmaps. - OPENSLIDE + OPENCV: Handle SVS/NDPI formats natively, implement multi-resolution pyramid loading to prevent memory overflow, and generate annotated overlays showing positive regions. - PYQT5 GUI: Create a Windows desktop app with drag-and-drop slide loading, side-by-side comparison view (AI vs manual diagnosis), and one-click CSV export of results with confidence scores for your statistical analysis. - MODEL RETRAINING MODULE: Include a simple interface where you can add new labeled slides to the training set and retrigger model fine-tuning without touching code. - DOCUMENTATION PACKAGE: Deliver a technical appendix with model architecture diagrams, confusion matrix, ROC curves, and dataset statistics formatted for thesis inclusion. I've built 3 medical imaging tools for radiology and pathology teams, including a melanoma detection system that achieved 94% sensitivity on unseen data. I don't take on projects where the requirements are vague - let's schedule a 20-minute call to review your sample slides and confirm format compatibility before I commit to a timeline.

@mkh559a5a1fd66e9

With over a decade of experience in full-stack development, my expertise aligns directly with your requirements for the AI HER2 Slide Diagnosis Software. I have an extensive background in Graphical User Interface, Image Processing and Machine Learning (using languages such as Python, along with TensorFlow and PyTorch that you mentioned). Combining these skills makes me perfectly suited to create a stable and user-friendly Windows desktop application as needed, integrated with a smooth capacity for you to keep adding training data when needed. Moreover, I bring to the table a profound sense of understanding about the sensitivities and cutting-edge demands of the medical field. My previous work in US HEALTHCARE and the use of cryptographic technologies make me aware of the privacy concerns that arise from this project so you can trust that I will handle your WSI data with utmost care. Given that we'll be dealing with diagnostic outcomes for tantalizingly important research like yours, I prioritize reliability, reproducibility and unmistakable documentation above all.

@techsty2014

Good to see this project, We will build your Windows desktop application for HER2 slide diagnosis: WSI stitching via grid mosaic, model training on your labeled cases, inference with confidence scoring, and paired result export for your thesis. For the model, we will use a patch-based classification pipeline on top of PyTorch. This lets us process large WSI files tile by tile without exhausting memory. It also makes adding new training data straightforward (drop new slides into a folder and retrain). A couple of quick things to confirm: 1) Which WSI format do your slides use (SVS, NDPI, or TIFF)? 2) Roughly how many labeled slides do you have for initial training? The number quoted here is a starting estimate. The exact cost and timeline will be confirmed after we go through the full scope together. Ready to start whenever you are. Faizan

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer(6+ yoe)& I can develop a Windows-based digital pathology application for HER2-positive breast carcinoma analysis,covering slide assembly,WSI handling,model training,inference,confidence reporting,human-vs-AI comparison & thesis-ready result Proposed Approach: -Data Preparation & Ingestion:Review HER2 protocols & utilize OpenSlide to stitch slide tiles into metadata-preserved grids,extracting high-quality tissue patches -Modeling & Explainability:Train patch-level CNN/Transformer architectures to aggregate slide-level HER2 predictions,generating visual heatmaps to highlight predictive regions -Application & Deployment:Develop a standalone PySide6 Windows .exe featuring dataset management,model inference,manual diagnosis comparison & structured CSV/Excel exports Relevant Experience: -Fetal Ultrasound AI:Built PyTorch/Streamlit applications for clinical scan processing,anatomical segmentation & automated biometry (HC/BPD/OFD) with visual overlays & report generation -Medical Image Segmentation:Enhanced model robustness through advanced mask post-processing,geometric contour fitting & rigorous quality validation against annotated datasets -Clinical Workflows:Engineered end-to-end pipelines handling DICOM ingestion,metadata extraction,model inference & clinician-facing diagnostic reports -Developed standalone Python applications transforming raw medical images into structured predictions with editable review interfaces & exports

@onkarkulkarni8

As a seasoned Python developer with over a decade of experience, my skills align perfectly with your AI HER2 Slide Diagnosis Software project. I've had significant exposure to Digital Pathology and Medical Image Analysis, making me well-versed in your project's core requirements. My team at MAK Online Solutions has built numerous Web and Mobile applications for complex data processing and analysis, emphasizing quality, performance, and security throughout. Leveraging my proficient knowledge in Python, PyTorch, as well as TensorFlow – the tried-and-tested tools in digital pathology – I'm confident in delivering a reliable and reproducible solution for you. Moreover, our strong command over different WSI formats (SVS, NDPI or TIFF) ensures we can provide efficient compatibility options just as you desire. At the same time, I emphasize the need for clear documentation since I understand its impact on long-term viability and academic credibility. The thorough user manual I will deliver will complement the clean GUI I'll create for easy use of the software. I'm committed to delivering not only the working application but also an easy-to-understand technical note that will help strengthen the statistical analysis section of your dissertation.

@axiomswb

Building your offline HER2-neu diagnostic application... I see you need a standalone Windows GUI for your MD Pathology thesis to ingest WSI formats, construct mosaic slides, and run inference against human diagnoses. Reproducibility, statistical export, and technical documentation for your methodology section are paramount. Here is my exact execution plan: WSI Ingestion: I will use OpenSlide and Python to ingest gigapixel SVS/NDPI files smoothly, paired with a custom mosaic patching script for spatial reconstruction. Model Architecture & Training: I will implement a PyTorch-based CNN (e.g., ResNet) for HER2 classification. The GUI will feature an "Append Data" button so you can easily retrain the model offline as you collect more slides. Inference & Export UI: I will build the GUI using PyQt5 (compiled to .exe). It will feature a side-by-side comparison panel displaying the AI's confidence score versus human input, with a 1-click export to CSV/Excel for your analysis. Thesis Handoff: Alongside the source code, I will write a precise technical methodology note (detailing architecture, epochs, and splits) for direct citation in your dissertation. Quick question: For the Gajra Raja Medical College dataset, are the slides primarily in .svs or .ndpi format, and do you already have annotated Regions of Interest (ROIs)? I am ready to sign the NDA and review your samples.

@Microlent

Developing a Windows desktop application for AI-based breast cancer diagnosis requires careful planning. We built a web scraping automation service for a client, efficiently extracting structured data from dynamic websites, https://www.freelancer.com/portfolio-items/11349812-web-scraping-automation. For your specific needs, a clean and intuitive GUI is crucial. I'd focus on creating a user-friendly interface for ingesting whole-slide images and training an AI model. I can put together a quick Windows Application for free so you can see the approach first. ~ Rajesh

@sonika1419

**DO NOT PAY ME UNTIL I COMPLETE! :)** Hello my valuable client :) My profile is new over here but I have 7 years of experience in this field. I have completely understood about your project. Also I will provide you free maintenance on your project for 1 year after project completion. I can definitely complete this in your timeframe. Give me one chance to prove myself. Hit the chat button to get started. If you will not like my work then you dont need to pay me any money so dont worry and have faith in me :) I am eagerly waiting for your message.

@dushyant317

Hello Dr. Verma, I can help build a Windows-based standalone application for digital pathology with WSI processing, HER2-positive model training, inference comparison, confidence scoring, exportable results, and an intuitive GUI. The solution can be developed using Python/PyTorch with a scalable architecture so additional datasets and retraining workflows can be added later without changing the core application.

@tamannarajput18

I am excited to submit my proposal for the development of your AI HER2 Slide Diagnosis Software. With strong expertise in AI-powered healthcare solutions, computer vision, and full-stack application development, I can build a robust platform capable of analyzing HER2 pathology slides with high accuracy and efficiency. The solution will include secure image upload, AI-based slide classification, result visualization, reporting, and an intuitive dashboard for medical professionals. I will focus on performance, scalability, data security, and compliance with healthcare standards. My approach includes model integration, rigorous testing, user-friendly design, and ongoing support to ensure reliable diagnostic assistance. I am committed to delivering a high-quality solution tailored to your clinical and research requirements.

@RajieE1912

I have thoroughly reviewed your MD thesis requirements and am fully prepared to develop a reliable, offline Windows desktop application to evaluate AI inference in HER2-neu positive breast carcinoma whole slide images (WSI). Using a robust Python architecture with PyTorch or TensorFlow, I will implement a clean graphical user interface that smoothly ingests standard digital pathology file formats, performs tile-based grid/mosaic processing, and displays diagnostic predictions alongside confidence metrics side-by-side with human evaluations. The platform will include structured CSV/Excel export features for your statistical dissertation models and a modular training path so you can scale your custom dataset later without rewriting the source code. I am fully committed to delivering the compiled offline executable (.exe), inline-commented repository files, a comprehensive user manual, and a citeable technical note outlining model architecture and metrics. I am ready to review and sign your non-disclosure agreement (NDA) immediately via the platform portal to securely inspect your sample slide formats and establish this professional research partnership

@AcuteTech06

Hello, I have experience in AI development and medical image processing applications. I can develop HER slide diagnosis software with accurate model integration, image analysis pipeline, and user-friendly interface. I will ensure secure, scalable, and well-tested implementation with proper documentation. Regards, Bharti.

@carlosjulianp

Hello Dr. Verma, I am interested in your project. I have experience in Python, computer vision, AI model development, image processing, and desktop application development. Recently, I worked on an AI-based computer vision project involving image analysis, model training, data processing, and graphical user interfaces. I can develop a Windows desktop application with an intuitive GUI that allows image loading, dataset management, model training, inference on new slides, confidence score visualization, and result export for statistical analysis. The system can also be designed to support future training data additions without requiring major code modifications. My focus is on delivering reliable, well-documented, and reproducible software. I can provide the compiled application, source code, user documentation, and technical documentation describing the model architecture and performance metrics. I would be happy to discuss your dataset, image formats, and project requirements in more detail. Best regards.

@bizvivo

I am an experienced biostatistician and software developer with a strong background in digital pathology, AI model deployment, and medical image analysis. Your thesis on HER2‑neu Positive Breast Carcinoma aligns closely with my expertise in building reproducible, research‑grade tools for clinical workflows. Relevant Experience: Developed AI‑powered WSI viewers with mosaic/grid stitching for histopathology datasets (SVS, NDPI, TIFF). Built offline Windows desktop applications (PyQt/Qt + Python) with intuitive GUIs for medical researchers, including export modules for statistical analysis. Designed training pipelines in PyTorch/TensorFlow for oncology datasets, with modular data‑ingestion so new cases can be added without code changes. Delivered AI‑assisted diagnostic tools with confidence scoring, side‑by‑side human vs. machine comparison, and optional heat‑map overlays. Authored technical notes and user manuals for academic citation, ensuring reproducibility and clarity in publications. Proposed Workflow: WSI ingestion + mosaic reconstruction. Training exclusively on HER2‑neu positive slides. Inference with GUI‑based comparison (AI vs. human). Export paired results (CSV/Excel) for statistical analysis.

@jabulanix

We've just completed a similar project, providing a seamless experience for a client in digital pathology with accurate results. We can assist you in developing the AI HER2 Slide Diagnosis Software with a smooth and professional process, ensuring your thesis requirements are met efficiently. While building my profile on Freelancer.com, I am offering discounted pricing for this project. If the final work does not meet the agreed project scope and approved requirements, payment is not required. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, Jabu.

@khushboo69

I'll develop a standalone Windows desktop application for your MD Pathology thesis that enables end-to-end HER2-neu positive breast carcinoma analysis using AI. The application will support importing WSI formats such as SVS, NDPI, or TIFF, and where multiple slide images are provided, it will first generate a digital whole-slide image using mosaic/grid stitching techniques before processing. The system will include a clean graphical interface for dataset management, model training, inference, result visualization, and export without requiring command-line interaction. Using Python with PyTorch and digital pathology libraries such as OpenSlide, the application will train exclusively on your HER2-neu positive cases and allow additional training data to be added later through the GUI. During inference, the software will display the AI prediction, confidence score, and human diagnosis side-by-side, with optional heatmaps or highlighted regions to improve interpretability. Results can be exported to Excel/CSV for statistical analysis and dissertation use. The final delivery will include a Windows installer, complete source code, user manual, and technical documentation covering model architecture, dataset preparation, training methodology, validation metrics, and reproducibility details suitable for thesis citation. The solution will be designed for offline operation, reliability, and future expansion as your research dataset grows.

@alluankit01

Hello Dr. Verma, Your thesis project is very interesting, and I would be happy to assist in developing a Windows-based AI application for HER2-neu positive breast carcinoma analysis. The solution can include: • Whole-slide image ingestion and preprocessing • Mosaic/grid-based slide reconstruction workflow • AI model training using provided HER2-positive datasets • Inference module with diagnosis and confidence scoring • Side-by-side comparison of AI and human assessments • Result export for statistical analysis and research documentation • User-friendly Windows GUI with no command-line dependency • Support for commonly used pathology image formats • Documentation covering training, inference, and system usage Workflow: • Dataset and format review • WSI processing and model integration • Desktop GUI development • Testing, validation, and export functionality • Documentation and deployment package preparation The application will be designed to support future dataset additions and retraining while maintaining reproducibility and ease of use for academic research. I would be happy to review sample slides and discuss the preferred workflow before development begins. Best Regards, Ankit