https://github.com/krishnaura45/tbp-skin-detect
⚔️ISIC Competition 🧪Feature Extraction and Ensemble
https://github.com/krishnaura45/tbp-skin-detect
binary-classification cancer catboost cross-validation custom-metrics global gpu image kaggle-competition lgbm optuna research-project stratified-k-fold xgboost
Last synced: 10 days ago
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⚔️ISIC Competition 🧪Feature Extraction and Ensemble
- Host: GitHub
- URL: https://github.com/krishnaura45/tbp-skin-detect
- Owner: krishnaura45
- License: cc0-1.0
- Created: 2025-04-18T05:29:17.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-06-06T14:39:10.000Z (5 months ago)
- Last Synced: 2025-06-06T15:35:30.183Z (5 months ago)
- Topics: binary-classification, cancer, catboost, cross-validation, custom-metrics, global, gpu, image, kaggle-competition, lgbm, optuna, research-project, stratified-k-fold, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 450 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# tbp-skin-detect
Skin Cancer Detection from 3D Total Body Photos
### **Project Duration**: Aug 15, 2024 - Sep 7, 2024
---
## 🧠 Objective
TThe goal was to build binary classifiers to predict malignant skin lesions from single-lesion crops extracted from 3D total body photos (TBP). This project is part of the `ISIC 2024 - Skin Cancer Detection from 3D-TBP` Kaggle competition. Submissions were evaluated on **partial AUC (pAUC)** for true positive rates (TPR) above **80%**.
---
## 🧩 Approach
You can explore the complete methodology in this notebook: 🔗 [ISIC24 - Heavy Feature Eng with Polars + Boosting + CV + Ensemble Blend](https://github.com/krishnaura45/tbp-skin-detect/blob/main/isic24-feature-boost-ensemble.ipynb)
Key steps followed:
- ✏️ **Feature Engineering**: *spatial insights*
- Extracted 3D landmark distances using pairwise Euclidean computations across TBP points.
- Constructed derived geometric features reflecting anatomical symmetry and patient-level spatial variation.
- ⚖️ **Patient-Level Normalization**: *consistency modeling*
- Applied normalization of features at the patient level to control for inter-subject variability.
- Included feature columns for image count per patient.
- 📊 **Categorical Handling**: *info retention*
- Employed OneHotEncoder for categorical variables.
- Converted them to category dtype for memory efficiency.
- 🧰 **Ensemble Learning**: *reducing model variance*
- Trained LightGBM, XGBoost, and CatBoost models independently.
- Combined predictions using a weighted ensemble method for improved pAUC.
- 📊 **Custom Evaluation Metric**: *tailored pAUC*
- Implemented a custom scoring function to simulate competition-specific pAUC above 80% TPR.
- This metric guided model selection and cross-validation.
---
## 🏆 Results / Outcomes
- ✅ Public Leaderboard Scores:
- 0.18368, 0.18412, 0.18519
- 🏁 Private Leaderboard Scores:
- 0.16733, 0.16753, **0.16930** (final best)
- 🥇 Rank Achieved:
- Placed `184th` out of **3410 participants** and **2739 teams** as a **solo competitor**
---
## 🔗 References
- 🏆 Kaggle Competition: [ISIC 2024 - Skin Cancer Detection with 3D-TBP](https://www.kaggle.com/competitions/isic-2024-challenge)
---
## 🛠️ Tech Stack
- **Language**: Python 🐍
- **Libraries**:
- `polars` for dataframe operations
- `pandas`, `numpy` for numerical tasks
- `stratifiedKFold` for cross validation
- `sklearn`, `lightgbm`, `xgboost`, `catboost` for modeling
- `matplotlib`, `seaborn` for visualization
- `optuna` for hyperparameter tuning
- **Tools**:
- Jupyter Notebook / Kaggle Notebooks 📓 for experimentation and code
- Custom Python metric functions for pAUC evaluation