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dsm:assignments:01 [2024/10/13 19:52] radu.chivereanu |
dsm:assignments:01 [2025/01/04 15:32] (current) emilian.radoi [Project] |
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| ===== Project ===== | ===== Project ===== | ||
| - | * **Team Composition**: Teams should consist of 2 members. | + | * **Team**: 2 members. |
| * **Project Selection**: | * **Project Selection**: | ||
| - | * **Option 1**: Choose from a list of pre-defined project ideas provided below. | + | * **Option 1**: Choose from the list of pre-defined project ideas provided below. |
| * **Option 2**: Propose your own project idea, which requires approval from the course team. | * **Option 2**: Propose your own project idea, which requires approval from the course team. | ||
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| | | ||
| === Project Workflow === | === Project Workflow === | ||
| - | Each team will: | + | Each team is required to: |
| * **Implement** their chosen project idea, building a model or system that addresses a specific medical data science problem. | * **Implement** their chosen project idea, building a model or system that addresses a specific medical data science problem. | ||
| * **Evaluate** their approach using appropriate metrics (accuracy, precision, recall, etc.), and compare results to existing state-of-the-art methods. | * **Evaluate** their approach using appropriate metrics (accuracy, precision, recall, etc.), and compare results to existing state-of-the-art methods. | ||
| - | * **Document** their progress and findings in both a formal report and presentation. | + | * **Document** their progress and findings in both a formal report and presentation, in **English**. |
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| === Milestones and Deliverables === | === Milestones and Deliverables === | ||
| - | == 1. Milestone 1 (M1) - Related Work and State-of-the-Art Review (1 point) == | + | == 1. M1 (04.11.24) - Related Work / State-of-the-Art Review (1p) == |
| * **Objective**: Define the research context by reviewing and summarising related work in the area you are addressing. | * **Objective**: Define the research context by reviewing and summarising related work in the area you are addressing. | ||
| * **Action Items**: | * **Action Items**: | ||
| - | * Conduct a thorough literature review of papers, articles, and studies relevant to your project. - 0.6 | + | * Conduct a thorough literature review of papers, articles, and studies relevant to your project. |
| - | * Summarise the current state-of-the-art methods in the field. - 0.2 | + | * Summarise the current state-of-the-art methods in the field. |
| - | * Identify gaps in the research or areas for potential improvement. - 0.2 | + | * Identify gaps in the research or areas for potential improvement. |
| - | * **Documentation**: Create a report section (minimum of 2 pages) detailing your findings, including citations of key papers and a summary of how your project will build upon or differ from existing work. | + | * **Documentation**: Create a report section (2 pages excluding references) detailing your findings, including citations of key papers and a discussion of how your project will build upon or differ from existing work, in **English**. |
| + | * **Upload Documentation**: [[https://curs.upb.ro/2024/mod/assign/view.php?id=49434|Upload]] (Must contain title and authors) | ||
| | | ||
| - | == 2. Milestone 2 (M2) - Dataset Collection and Baseline Results (1 point) == | + | == 2. M2 (18.11.24) - Dataset Collection and Baseline Results (1p) == |
| * **Objective**: Obtain the datasets required for your project and implement a baseline model for comparison. | * **Objective**: Obtain the datasets required for your project and implement a baseline model for comparison. | ||
| * **Action Items**: | * **Action Items**: | ||
| - | * **Dataset Collection** (0.6): | + | * **Dataset Collection**: |
| * Obtain a relevant dataset, either from the provided resources or other public sources (e.g., Kaggle, UCI, Papers with Code). | * Obtain a relevant dataset, either from the provided resources or other public sources (e.g., Kaggle, UCI, Papers with Code). | ||
| - | * Preprocess the data (e.g., cleaning, normalization, dealing with missing values, etc.). | + | * Preprocess the data (e.g., cleaning, normalization, dealing with missing values). |
| - | * **Baseline Model** (0.4): | + | * **Baseline Model**: |
| - | * Implement at least one baseline method (e.g., logistic regression, support vector machines, or a simple neural network). | + | * Implement at least one baseline method (e.g., logistic regression, support vector machines, a simple neural network). |
| * Obtain preliminary results to compare against future improvements. | * Obtain preliminary results to compare against future improvements. | ||
| * **Evaluation Metrics**: Choose appropriate metrics (e.g., accuracy, F1-score, ROC-AUC) and document initial performance. | * **Evaluation Metrics**: Choose appropriate metrics (e.g., accuracy, F1-score, ROC-AUC) and document initial performance. | ||
| - | * **Documentation**: Submit a report section (minimum of 2 pages) describing the dataset, preprocessing steps, baseline model, and results. | + | * **Documentation ** (IEEE conference paper format): Submit a report section (2 pages excluding references) describing the dataset, preprocessing steps, baseline model, and results. |
| + | * **Upload Documentation**: [[https://curs.upb.ro/2024/mod/assign/view.php?id=49438|Upload]] | ||
| - | == 3. Milestone 3 (M3) - Own Contribution (1 point) == | + | == 3. M3 (18.12.24) - Own Contribution (1p) == |
| * **Objective**: Implement your novel contribution to the field, either by solving a new problem or improving an existing method. | * **Objective**: Implement your novel contribution to the field, either by solving a new problem or improving an existing method. | ||
| * **Types of Contributions**: | * **Types of Contributions**: | ||
| - | * **Address a New Problem**: Tackle a medical data science issue that hasn’t been addressed by related work. | + | * Address a New Problem: Tackle a medical data science issue that has not been addressed by related work. |
| - | * **Improve Existing Methods**: | + | * Improve Existing Methods: |
| - | * **Improve Results**: Enhance the performance of an existing solution by optimising models or experimenting with different techniques. | + | * Improve Results: Enhance the performance of an existing solution by optimising models or experimenting with different techniques. |
| - | * **Extensive Experiments**: Conduct extensive experiments to assess your model’s robustness, including testing with different datasets or under varying conditions. | + | * Extensive Experiments: Conduct extensive experiments to assess your model’s robustness, including testing with different datasets or under varying conditions. |
| - | * **New Approach**: Introduce a new method or architecture (e.g., switching from traditional CNNs to transformers), even if it doesn’t outperform state-of-the-art methods, as long as it provides a novel perspective. | + | * New Approach: Introduce a new method or architecture (e.g., switching from traditional CNNs to transformers), even if it does not outperform state-of-the-art methods, as long as it provides a novel perspective. |
| - | * **Examples**: | + | * Examples: |
| * Use a different deep learning architecture (e.g., ResNet vs. EfficientNet). | * Use a different deep learning architecture (e.g., ResNet vs. EfficientNet). | ||
| * Apply a novel training strategy, such as self-supervised learning or data augmentation techniques. | * Apply a novel training strategy, such as self-supervised learning or data augmentation techniques. | ||
| * Propose a hybrid model that combines multiple approaches (e.g., combining CNNs with decision trees). | * Propose a hybrid model that combines multiple approaches (e.g., combining CNNs with decision trees). | ||
| - | * **Documentation**: Write a report section (minimum of 2 pages) detailing your contribution, how it differs from existing work, and your experimental results. | + | * **Documentation** (IEEE conference paper format): Write a report section (2 pages excluding references) **justifying your chosen approach**, detailing your contribution, how it differs from existing work, and comparing your experimental results to the baseline and state of the art. |
| + | * **Upload Documentation and code**: [[https://curs.upb.ro/2024/mod/assign/view.php?id=49446|Upload]] | ||
| - | == 4. Milestone 4 (M4) - Project Report (1 point) == | + | == 4. M4 (08.01.25) - Project Report (1p) == |
| * **Objective**: Compile your project into a well-organised academic report. | * **Objective**: Compile your project into a well-organised academic report. | ||
| * **Action Items**: | * **Action Items**: | ||
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| * **Abstract**: Briefly summarize your project, contributions, and key findings. | * **Abstract**: Briefly summarize your project, contributions, and key findings. | ||
| * **Introduction**: Explain the problem you are addressing, motivation, and background. | * **Introduction**: Explain the problem you are addressing, motivation, and background. | ||
| - | * **Related Work**: Include the summary from Milestone 1. | + | * **Related Work**: Include the summary from M1. |
| * **Methodology**: Detail your approach, including algorithms, models, and techniques used. | * **Methodology**: Detail your approach, including algorithms, models, and techniques used. | ||
| - | * **Experiments**: Describe the datasets, baseline methods, and results from Milestone 2. | + | * **Experiments**: Describe the datasets, baseline methods, and results from M2. |
| - | * **Own Contribution**: Document your original contribution, as outlined in Milestone 3. | + | * **Own Contribution**: Document your original contribution, as outlined in M3. |
| * **Results and Discussion**: Present detailed results with visualizations (graphs, tables) and discuss their implications. | * **Results and Discussion**: Present detailed results with visualizations (graphs, tables) and discuss their implications. | ||
| * **Conclusion**: Summarize the outcomes, limitations, and future work. | * **Conclusion**: Summarize the outcomes, limitations, and future work. | ||
| - | * **Documentation**: Submit a polished, formal academic report in IEEE format (6-8 pages). | + | * **Documentation**: Submit a polished, formal academic report in IEEE format (8 pages excluding references). |
| + | * **Upload Documentation**: [[https://curs.upb.ro/2024/mod/assign/view.php?id=49445|Upload]] | ||
| - | == 5. Milestone 5 (M5) - Project Presentation** (percentage-based grading) == | + | == 5. M5 (08.01.25) - Project Presentation (percentage-based grading) == |
| * **Objective**: Present your project and findings to the class. | * **Objective**: Present your project and findings to the class. | ||
| * **Action Items**: | * **Action Items**: | ||
| - | * Prepare a 10-15 minute presentation covering: | + | * Prepare a 6 minute presentation covering: |
| * The problem you addressed and its relevance. | * The problem you addressed and its relevance. | ||
| * Key steps of your methodology. | * Key steps of your methodology. | ||
| * Experimental results and key contributions. | * Experimental results and key contributions. | ||
| * Conclusions and potential areas for future research. | * Conclusions and potential areas for future research. | ||
| - | * Create slides with clear visuals, including figures, graphs, and performance metrics. | + | * Create well- polished slides with clear visuals, including figures, graphs, and performance metrics. |
| - | * **Evaluation**: Your presentation will be graded based on clarity, depth of explanation, and the quality of results. The final project grade will be weighted by your presentation quality. | + | * **Evaluation**: Your presentation will be graded based on clarity, depth of explanation, the quality of results and the Q&A section. The final project grade will be weighted by your presentation quality. |
| + | * **Upload Presentation**: [[https://curs.upb.ro/2024/mod/assign/view.php?id=49444|Upload]] | ||
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| * **Proposed Datasets**: MURA, RSNA etc. | * **Proposed Datasets**: MURA, RSNA etc. | ||
| - | === 5. Diabetes Detection === | + | === 5. Cancer Detection from Histopathology Images === |
| - | * **Objective**: Predict whether a patient has diabetes based on features like blood glucose levels and other health indicators. | + | |
| - | * **Potential Contribution**: Compare various machine learning techniques (e.g., logistic regression, decision trees, or neural networks). | + | |
| - | * **Proposed Dataset**: [[https://www.kaggle.com/datasets/mathchi/diabetes-data-set/data|Diabetes Dataset]]. | + | |
| - | + | ||
| - | === 6. Heart Disease Prediction === | + | |
| - | * **Objective**: Predict the likelihood of heart disease based on patient medical data. | + | |
| - | * **Potential Contribution**: Apply feature engineering and machine learning models (random forests, logistic regression) to improve prediction accuracy. | + | |
| - | * **Proposed Dataset**: UCI Heart Disease dataset. | + | |
| - | + | ||
| - | === 7. COVID-19 Severity Prediction === | + | |
| * **Objective**: Predict the severity of COVID-19 cases using patient data such as demographics, clinical tests, and symptoms. | * **Objective**: Predict the severity of COVID-19 cases using patient data such as demographics, clinical tests, and symptoms. | ||
| * **Potential Contribution**: Use gradient boosting, random forests, or deep learning to predict hospitalization risk. | * **Potential Contribution**: Use gradient boosting, random forests, or deep learning to predict hospitalization risk. | ||
| - | * **Proposed Dataset**: Gather publicly available COVID-19 datasets (e.g., from Kaggle). | + | * **Proposed Dataset**: Choose an appropriate one from [[https://github.com/maduc7/Histopathology-Datasets|maduc7/Histopathology-Datasets]] |
| - | === 8. Alzheimer’s Disease Progression Prediction === | + | === 6. Alzheimer’s Disease Progression Prediction === |
| * **Objective**: Predict the progression of Alzheimer’s disease using imaging (e.g., MRI) or genetic data. | * **Objective**: Predict the progression of Alzheimer’s disease using imaging (e.g., MRI) or genetic data. | ||
| * **Potential Contribution**: Train deep learning models (e.g., 3D CNNs) to predict cognitive decline. | * **Potential Contribution**: Train deep learning models (e.g., 3D CNNs) to predict cognitive decline. | ||
| - | * **Proposed dataset**: [[https://www.kaggle.com/datasets/ninadaithal/imagesoasis|OASIS Alzheimer's Detection]]. | + | * **Proposed dataset**: [[https://www.kaggle.com/datasets/ninadaithal/imagesoasis|OASIS Alzheimer's Detection.]] |
| - | === 9. Seizure Detection from EEG Data === | + | === 7. Interpretation of Knee MRI === |
| - | * **Objective**: Detect epileptic seizures using EEG data to support automated diagnosis. | + | * **Objective**: Develop models for automated interpretation of knee MRs. |
| - | * **Potential Contribution**: Train machine learning models to analyze EEG signals for seizure detection. | + | * **Potential Contribution**: Use transfer learning for improved performance on this dataset. |
| - | * **Proposed datasets**: [[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10235576/|Here]]. | + | * **Proposed dataset**: [[https://www.kaggle.com/datasets/cjinny/mrnet-v1|MRNet - Kaggle.]] / [[https://stanfordmlgroup.github.io/competitions/mrnet/|MRNet]] |
| - | === 10. Your own project === | + | === 8. Your own project === |
| * **Objective**: - | * **Objective**: - | ||
| * **Potential Contribution**: - | * **Potential Contribution**: - | ||
| * **Proposed datasets**: - | * **Proposed datasets**: - | ||
