🎓 Applications open: AI-Driven Tumor Classification from Histopathological Images Using Deep Convolutional Neural Networks
100 Days Project + Additional access upto one Year
STANDARD OPERATING PROCEDURE
Computational Pathology | CNN Based Cancer Subtyping
A complete phase by phase execution guide for students to replicate, extend, and publish real-world medical imaging research using transfer learning & EfficientNet architecture
7
PHASES
3
DELIVERABLES
2
DATASETS
1
PUBLICATION
Program Objectives
Mission Statement:
Guide students and professionals beyond passive learning - enabling them to replicate a real computational pathology study on BreakHis + UBC cancer Datasets using EfficientNet framworks and transfer learning, then deeply understand, analyze, improve and ultimately publish the findings backed by a reproducible GitHub code.
1. Research Foundation and Data Understanding (Days 1 to 10)
Build domain awareness and full comprehension of the target study before touching any code
2. Data Acquisition and Understanding (Days 11 to 20)
Source, explore and document both histopathology datasets with ptofessional rigor
BreakHis Dataset Download using Kaggle API
Folder Structure
Class Dristribution
UBC Dataset Download using Kaggle API
Image Visualization
Scanning Different Magnifications
Label Extraction
Dataset Summary
Data Audit Report
3. Data Pre-Processing and Augmentation (Days 21 to 35)
Transform raw images into a clean, model-ready pipeline with robust augmentation
Rezising the image for optimization
ImageNet Normalization
Train/Val/Test Split
Stratified sampling v/s Patient level sampling
Lebel Encoding
One-hot Encoding
Augmentation Pipeline
Flip Rotate Zoom
Class Imbalance Handling
4. Model Building and Training (Days 36-55 | Core phase)
Implement EfficientNet transfer learning with two stage fine-tuning across both cancer datasets.
EfficientNetB0 Architecture
Pretrained ImageNet Weights
Classification Head
Loss and Optimizer setup
Stage 1: Frozen Backbone
Feature Extraction
Overfitting Detection
Stage 2: Fine Tuning
Unfreeze Deep layers
Learning Rate Tuning
Early Stopping and Callbacks
Model Checkpointing
Ovarian Dataset Training
Training Documentation
5. Model Building and Training (Days 56-70)
Deep Quantitative analysis and publication grade reporting of model performance
Accuracy Metrics
Precision/Recall/F1
Confusion Matrix
ROC-AUC Curves
Magnification Comparision
Weak Class Analysis
Missclassification Studies
Visualization plots
Publication Ready Tables
Evaluation Report
6. Advanced Insight and Improvements (Days 71-80)
Think like a researcher - propose novel improvements and benchmark against literature.
Magnification Impact
Imbalance Strategies
Focal Loss Concept
Stain Normalization
Multi-Scale Learning
Error Deep-Dive`
Improvement Proposals
Literature Comparison
Discussion Draft
7. Research Writing and Publication Assistance (Days 81-100)
Compose, refine and submit a complete research paper - while building professional GitHub portfolio and presentation
Flexible till the manuscript is ready
GitHub Repository and Code Documentation
Skills Gained:
1. Medical Imaging
2. Deep Learning
3. Model Evaluation
4. Research Writing
5. Reproducibility
Final Deliverables:
1. Research Paper
2. GitHub Project
3. Final Presentation