• Team: 2 members.
• Project Selection:
  • 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.

Each team is required to:

• 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.
• Document their progress and findings in both a formal report and presentation, in English.

• Objective: Define the research context by reviewing and summarising related work in the area you are addressing.
  • Action Items:
    • 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.
    • Identify gaps in the research or areas for potential improvement.
  • 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: Upload (Must contain title and authors)

• Objective: Obtain the datasets required for your project and implement a baseline model for comparison.
  • Action Items:
    • Dataset Collection:
      • 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).
    • Baseline Model:
      • 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.
    • Evaluation Metrics: Choose appropriate metrics (e.g., accuracy, F1-score, ROC-AUC) and document initial performance.
  • Documentation (IEEE conference paper format): Submit a report section (2 pages excluding references) describing the dataset, preprocessing steps, baseline model, and results.
  • Upload Documentation: Upload

• Objective: Implement your novel contribution to the field, either by solving a new problem or improving an existing method.
  • Types of Contributions:
    • Address a New Problem: Tackle a medical data science issue that has not been addressed by related work.
    • Improve Existing Methods:
      • 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.
      • 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:
          • 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.
          • Propose a hybrid model that combines multiple approaches (e.g., combining CNNs with decision trees).
  • 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: Upload

• Objective: Compile your project into a well-organised academic report.
  • Action Items:
    • Write a research-style report following the IEEE conference paper format.
    • Structure:
      • Abstract: Briefly summarize your project, contributions, and key findings.
      • Introduction: Explain the problem you are addressing, motivation, and background.
      • Related Work: Include the summary from M1.
      • Methodology: Detail your approach, including algorithms, models, and techniques used.
      • Experiments: Describe the datasets, baseline methods, and results from M2.
      • Own Contribution: Document your original contribution, as outlined in M3.
      • Results and Discussion: Present detailed results with visualizations (graphs, tables) and discuss their implications.
      • Conclusion: Summarize the outcomes, limitations, and future work.
  • Documentation: Submit a polished, formal academic report in IEEE format (8 pages excluding references).
  • Upload Documentation: Upload

• Objective: Present your project and findings to the class.
  • Action Items:
    • Prepare a 6 minute presentation covering:
      • The problem you addressed and its relevance.
      • Key steps of your methodology.
      • Experimental results and key contributions.
      • Conclusions and potential areas for future research.
    • 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, the quality of results and the Q&A section. The final project grade will be weighted by your presentation quality.
  • Upload Presentation: Upload

• Total Points = (M1 + M2 + M3 + M4) x M5
  • Each milestone (M1 to M4) will be awarded a maximum of 1 point.
  • M5 (presentation) will serve as a multiplier (e.g., if your presentation quality is evaluated at 90%, the total score from M1 to M4 will be multiplied by 0.9).

• Objective: Detect posture abnormalities from videos or images and suggest exercises to correct them.
• Potential Contribution: Develop a system using deep learning (e.g., CNNs or pose estimation algorithms like OpenPose) to detect poor posture.
• Relevant work: Posture Detection.

• Objective: Identify whether a person is a smoker based on lung capacity, voice analysis, or X-ray images.
• Potential Contribution: Use CNNs for X-ray image analysis or apply audio signal processing methods to detect smoking patterns from voice recordings.
• Dataset: Gather data from publicly available voice or medical image datasets.
• Note: Each of the modalities (audio, video, image) chosen, or their combination may result in a different project, without much overlap.

• Objective: Detect retinal lesions from medical images, aiding early diagnosis of conditions like diabetic retinopathy.
• Potential Contribution: Develop a novel segmentation or classification approach using CNNs or U-Net models.
• Proposed Dataset: Retinal Lesions Dataset.

• Objective: Develop a model that identifies fractures in X-ray images, which could help radiologists in making faster diagnoses.
• Potential Contribution: Train a CNN or use a pre-trained model (e.g., ResNet) to classify fracture types.
• Proposed Datasets: MURA, RSNA etc.

• 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.
• Proposed Dataset: Choose an appropriate one from maduc7/Histopathology-Datasets

• 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.
• Proposed dataset: OASIS Alzheimer's Detection.

• Objective: Develop models for automated interpretation of knee MRs.
• Potential Contribution: Use transfer learning for improved performance on this dataset.
• Proposed dataset: MRNet - Kaggle. / MRNet

• Objective: -
• Potential Contribution: -
• Proposed datasets: -