685.801.21 - Independent Study in Data Science I

Course Structure

Milestones:
- Weeks 1–2: Literature review (CNNs, multimodal, latent models).
- Weeks 3–4: Dataset preparation, baseline model training.
- Weeks 5–6: Design and implement latent variable model.
- Weeks 7–8: Comparative experiments with Hugging Face/timm frameworks.
- Weeks 9–10: Draft paper, integrate results and analysis.
- Week 11–12: Finalize paper, submit deliverables, prepare for SPIE.

Course Topics

This independent study investigates latent-variable multimodal models for melanoma detection, emphasizing integration of lesion images and patient metadata into patient-level representations. The study begins with a CNN+metadata baseline and expands to comparisons with pretrained frameworks (Hugging Face models such as ResNet and EfficientNet, timm, and similar libraries).

The primary dataset is SIIM-ISIC melanoma detection; however, flexibility is included to substitute or supplement with other public biomedical or multimodal datasets if coverage or research depth requires expansion.

Evaluation will follow standard ML metrics (ROC AUC, precision, recall, confusion matrices). The aim is to determine whether latent variable integration improves diagnostic accuracy beyond image-only classifiers and to critically assess applicability in real-world healthcare contexts.

This project is aligned with preparation for submission to SPIE Defense + Security 2026, meeting guidelines for structured abstracts and paper submissions.

Course Goals

Learning Objectives:
- Gain proficiency in latent variable modeling and patient-level aggregation strategies.
- Implement a CNN+metadata architecture, and extend to comparisons with Hugging Face and other frameworks.
- Evaluate models with standard metrics and interpretability tools.
- Draft a research-style paper suitable for SPIE submission.

Deliverables:
- SPIE Abstract (due October 15, 2025) for mid-semester evaluation.
- Final research paper/report (end of semester), prepared to SPIE standards.
- GitHub repository with training and evaluation pipelines.
- Final presentation to mentor/advisor.
- If needed, revisions during winter break based on SPIE editorial feedback.

Milestones:
- Weeks 1–2: Literature review (CNNs, multimodal, latent models).
- Weeks 3–4: Dataset preparation, baseline model training.
- Weeks 5–6: Design and implement latent variable model.
- Weeks 7–8: Comparative experiments with Hugging Face/timm frameworks.
- Weeks 9–10: Draft paper, integrate results and analysis.
- Week 11–12: Finalize paper, submit deliverables, prepare for SPIE.

Textbooks

No Textbook, Lecture Note Only

Required Software

Python, Jupyter Notebook, Google Colab, Google Cloud Storage, and Git Hub

Student Coursework Requirements

- Weeks 1–2: Literature review (CNNs, multimodal, latent models).
- Weeks 3–4: Dataset preparation, baseline model training.
- Weeks 5–6: Design and implement latent variable model.
- Weeks 7–8: Comparative experiments with Hugging Face/timm frameworks.
- Weeks 9–10: Draft paper, integrate results and analysis.
- Week 11–12: Finalize paper, submit deliverables, prepare for SPIE.

Grading Policy

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students. This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements. For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.  For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.    If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.