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English
$
USD
I will build medical image preprocessing, CNN classification, and xai pipelines
Research enthusiast
like to read book
About this Gig
Need a robust medical imaging pipeline from raw scanner outputs to trained neural networks?
Raw scans (DICOM / NIfTI) cannot be fed directly into deep learning models without proper spatial alignment and intensity normalization. I build complete medical computer vision pipelineshandling data preprocessing, custom architecture design, and model interpretability.
1. Data Preprocessing:
- DICOM to NIfTI format conversion
- Spatial reorientation (RAS coordinate system)
- N4 Bias Field Correction & Skull Stripping
- MNI152 Rigid Registration & Intensity Normalization
- 2D Slice Extraction (Axial, Coronal, Sagittal)
2. Deep Learning & Classification:
- Custom CNNs & Hybrid Architectures (PyTorch / TensorFlow)
- Class imbalance mitigation (Weighted Random Sampling / custom losses)
- Performance reporting (Accuracy, Precision, Recall, Macro F1-Score, Confusion Matrix)
3. Explainable AI (XAI):
- Grad-CAM and Grad-CAM++ visual heatmaps
- Feature activation plots highlighting Regions of Interest (ROI)
Deliverables:
- Clean Python modular source code / Jupyter Notebooks
- Trained model weights (.pt or .h5) and classification reports
Please message me before ordering to discuss your dataset and requirements!
FAQ
What medical image formats can you handle?
I can process standard raw medical imaging formats including DICOM (.dcm), NIfTI (.nii, .nii.gz), NRRD, as well as 2D image formats (PNG, JPEG, TIFF). Whether your dataset consists of 3D volumetric scans or extracted 2D slice sets, I can build an automated pipeline to handle it.
Does your pipeline include preprocessing like skull stripping or bias correction?
Yes. Raw medical scans require standardization before being fed into deep learning models. My preprocessing workflow covers DICOM-to-NIfTI conversion, spatial reorientation (RAS alignment), N4 bias field correction, skull stripping/brain extraction, MNI152 registration, and intensity normalization (
Which deep learning frameworks and libraries do you use?
I primarily build modular, reproducible pipelines in Python using PyTorch, OpenCV, SimpleITK, NiBabel, NumPy, and scikit-learn. If you specifically require TensorFlow or Keras, I can adapt the model architecture to your preferred framework upon request.
How do you handle class imbalance in medical datasets?
Imbalanced classes (e.g., fewer diseased samples than normal controls) can severely bias deep learning models. I mitigate this using technical strategies such as Weighted Random Sampling during batch loader creation, class-weighted cross-entropy loss functions, and data augmentation techniques to en
What is Explainable AI (Grad-CAM++), and why do I need it?
Deep neural networks are often criticized for being "black boxes." Grad-CAM++ generates class-activation heatmaps directly over your MRI/CT scans to visually highlight the exact brain or anatomical regions (such as tissue atrophy or enlarged ventricles) driving the model's prediction. This interpret
Do you provide medical diagnosis or clinical patient evaluation?
No. In full compliance with Fiverr guidelines, I do not provide clinical diagnostic services, patient consultation, or medical advice. My services are limited strictly to software engineering, data preprocessing, machine learning model architecture, and academic/R&D technical assistance.
What deliverables will I receive when the order is completed?
Depending on your selected package, you will receive fully modular, clean Python source code (or Jupyter Notebooks), trained model weight files (.pt or .h5), dataset preprocessing scripts, visual evaluation outputs (Confusion Matrix, Loss/Accuracy Curves, ROC curves), and Grad-CAM++ heatmaps.
Can we sign an NDA or ensure my proprietary research dataset stays private?
Absolutely. I take data privacy seriously. Your dataset and project code will remain 100% confidential and will never be shared, published, or re-used in any form. I am fully open to signing a Non-Disclosure Agreement (NDA) before you send the files.
