MRI Brain Tumor Detection & Segmentation

Architecture

Overview

Built a production-ready multimodal MRI classification and segmentation pipeline trained on the BraTS dataset. The model uses a shared encoder architecture for both tumor classification and pixel-level segmentation, achieving strong performance across both tasks simultaneously.

Results

Key Features

• Shared Encoder Architecture: Single backbone for both classification and segmentation reduces parameter count and inference time
• Conditional Segmentation: Segmentation head activates only when tumor is detected, optimizing inference
• One-Command Pipeline: Productized training-to-demo pipeline with automation across 6 stages, 4 smart prompts, and 25/25 passing tests
• API Endpoints: 11 FastAPI endpoints for model inference, health checks, and data management

Inference Pipeline

flowchart LR
A[MRI Input] --> B[Preprocessing]
B --> C[Shared Encoder]
C --> D{Tumor?}
D -->|Yes| E[Segmentation Head]
D -->|No| F[No Tumor]
E --> G[Pixel Mask]
C --> H[Classification Head]
H --> D

Conditional segmentation activates only when tumor is detected, avoiding unnecessary compute on negative cases.

Tech Stack

• ML: PyTorch, torchvision, BraTS dataset
• API: FastAPI, Uvicorn, Pydantic
• Testing: pytest with 25/25 test coverage
• Optimization: Conditional inference, shared encoder, mixed precision