Overview

CSCI 3397/PSYC 3317 is an advanced undergraduate-level class organized around building a biomedical image analysis startup prototype. We move through four themes: image processing (weeks 1–4), deep learning (weeks 5–10), foundation models (weeks 11–13), and agentic systems (weeks 14–16).

Schedule



Staff & Office Hours


Theme Date Topic Materials Assignments

Theme 1: Digital Image Processing

Image Basics
[Week 1]		 Mon, Jan. 12 Lec. 1: Introduction
overview, motivation, syllabus
AI in healthcare, microscopy, radiology
lab0 out
Wed, Jan. 14 Lec. 2: Image Basics
light, object, camera, physics
microscopy, X-ray/CT, MRI, ultrasound imaging
Fri, Jan. 16 Lec. 3: Digital Image Basics
matrix, python libraries
loading and visualizing medical scans
lab0 due
No Class (Martin Luther King, Jr. Day)
Filtering
[Week 2-3]		 Wed, Jan. 21 Lec. 4: Pixel-level Processing
auto-contrast, thresholding
contrast enhancement for histology slides
Fri, Jan. 23 Lec. 5: Patch-level Processing
filtering, padding
denoising fluorescence microscopy images
Mon, Jan. 26 No Class (Snow Day) lab1 due
Wed, Jan. 28 Lec. 6: Convolution
convolution = linear+shift-invariant
impulse/box/Gaussian
smoothing MRI scans, noise reduction
Fri, Jan. 30 Lec. 7: More Filters
derivative: edge, laplacian
diffusion: Gaussian, Bilateral
edge detection in cell boundaries
  • [T] Chap. 4.2.3, 5.1
Applications
[Week 4]		 Mon, Feb. 2 Lec. 8: Image Transformation
point cloud, image warping
aligning serial tissue sections
lab2 due
Wed, Feb. 4 Lec. 9: Transformation Estimation
Linear regression, RANSAC
stitching whole-slide microscopy images
Fri, Feb. 6 Lec. 10: Image Segmentation
semantic, instance, panoptic
cell/organelle segmentation in EM
lab3 due

Theme 2: Deep Learning

CNN-based
Models
[Week 5-7]		 Mon, Feb. 9 Lec. 11: ML Basics
splits, leakage, metrics; baseline mindset
patient-level splits in clinical datasets
  • [F] Chap. 1.1
  • Coding: scikit-learn
 ps1 due
Wed, Feb. 11 Lec. 12: Linear Regression
linear regression; basis functions; regularization
predicting cell count from image features
Fri, Feb. 13 Lec. 13: Binary Linear Classification
Model: logistic regression
Loss: binary cross-entropy
Optimizer: SGD
malignant vs. benign classification
  • [F] Chap. 4.1-4.3
Mon, Feb. 16 Lec. 14: Multi-class Linear Classification
Model: softmax
Loss: cross-entropy
Optimizer: Momentum
tissue type classification in pathology
  • [F] Chap. 4.1-4.3
 lab4 due
ps1 out
Wed, Feb. 18 Lec. 15: Multilayer Perceptron (MLP)
Pytorch/GPU Basics
Model: nonlinear activation function
skin lesion classification (dermoscopy)
  • [F] Chap. 4.1-4.3
Fri, Feb. 20 Lec. 16: Backpropagation
Optimization: chain rule, dynamic programming
training networks on biomedical benchmarks
Mon, Feb. 23 No Class (Snow Day)
Wed, Feb. 25 Lec. 17: Convolutional Neural Network (CNN)
convolutional layers, pooling layers
chest X-ray screening
  • [F] Chap. 3.4, 4.5
 lab5 due
Fri, Feb. 27 Lec. 18: Modern CNNs
model: ResNet, ConvNext, MedConvNext
retinal disease grading (fundoscopy)
fp proposal due
No Class (Spring Vacation: Mar. 2–7)
Applications
[Week 9]		 Mon, Mar. 9 Lec. 19: Image-based Prediction
transfer learning, data augmentation, training tips
fine-tuning ImageNet models on histology
Wed, Mar. 11 Lec. 20: Object Detection
bounding boxes, R-CNN/YOLO; mAP metric
lung nodule detection in CT
lab6 due
Fri, Mar. 13 Lec. 21: Object Segmentation
encoder-decoder (U-Net); Dice/Focal loss
organ segmentation in CT/MRI
  • [F] Chap. 6.4
  • Paper: U-Net
 ps2 due
Mon, Mar. 16 Lec. 22: Image Generation
VAE, GAN basics; synthetic biomedical samples
augmenting rare disease datasets
  • [F] Chap. 5.1-5.3
  • Paper: GAN, VAE
Transformer-based Models
[Week 10]		 Wed, Mar. 18 Lec. 23: Attention Module
self-attention basics; MIL for pathology
whole-slide image classification
lab7 due
Fri, Mar. 20 Lec. 24: Transformer Model
tokenization, positional encoding, multi-head attention
3D medical image analysis
  • [F] Chap. 4.8-4.10, 5.3
  • Paper: ViT
 fp check-in (baseline)
Mon, Mar. 23 Lec. 25: Transformer Models in Vision
ViT, hybrid models; image patches as tokens
multi-scale pathology feature extraction
ps3 out (foundation models)

Theme 3: Foundation Models

FM Basics
[Week 11]		 Wed, Mar. 25 Lec. 26: Foundation Models Overview
pretrain-then-adapt paradigm; scaling; benchmarks
biomedical benchmarks (MedMNIST, CheXpert)
lab8 due
Fri, Mar. 27 Lec. 27: Self-Supervised Learning
contrastive learning, masked modeling (MAE, DINO)
pretraining on unlabeled pathology/radiology data
Mon, Mar. 30 Lec. 28: Parameter-Efficient Tuning (PEFT)
adapters, LoRA; efficient domain adaptation
adapting foundation models to new organs/stains
Wed, Apr. 1 Lec. 29: Promptable Segmentation
SAM-style prompting; interactive labeling
click-to-segment tumors with MedSAM
lab9 due
No Class (Good Friday Apr. 3) / No Class (Easter Monday Apr. 6)
Multimodal FMs
[Week 13]		 Wed, Apr. 8 Lec. 30: Multimodal Foundation Models
CLIP, image-text alignment; vision-language models
zero-shot medical image retrieval
ps3 due
Fri, Apr. 10 Lec. 31: Multimodal FMs in Biomedicine
PathChat, BiomedCLIP; report generation, visual Q&A
pathology Q&A and radiology report drafting

Theme 4: Agentic Systems

LLM Tooling + RAG
[Week 14]		 Mon, Apr. 13 Lec. 32: LLM APIs & Prompt Engineering
API calls, prompt design, structured outputs, tool calling
extracting structured findings from reports
ps4 out (agentic systems)
Wed, Apr. 15 Lec. 33: RAG & Document Search
embeddings, vector search, retrieval-augmented generation
searching medical literature by case similarity
  • Tutorial: LlamaIndex
  • Concept: embeddings, chunking, vector databases
 lab10 due
Fri, Apr. 17 Lec. 34: Coding Agents for Data Analysis
code generation, tool use, iterative analysis pipelines
automated cell counting and statistics
No Class (Patriot's Day: Mon Apr. 20)
Agentic Systems
[Week 15]		 Tue, Apr. 21
(Mon class)		 Lec. 35: Agentic Radiology/Pathology Workflows
automated reading, report drafting, quality checks
end-to-end slide screening → diagnosis pipeline
  • Case study: automated radiology report drafting
  • Case study: pathology slide screening pipeline
Wed, Apr. 22 Lec. 36: Deploying a Biomedical AI Product
latency, monitoring, HIPAA/privacy; live demo practice
deploying a clinical decision-support tool
ps4 due
fp slide due

Final Projects

Presentation Fri, Apr. 24 Final Project: Presentation I
Mon, Apr. 27 Final Project: Presentation II
Wed, Apr. 29 Final Project: Presentation III + Wrap-up
Submission Sun, May. 10 Final project report/code fp report/code due
Instructor
Name Office hours
Donglai Wed 2:30-3:30pm @ 245 Beacon Rm. 528F, Thu 2:30-3:30pm on zoom
  • Office hours will take place in person (or Zoom if needed).


Course information

This is a challenging course and we are here to help you become a more-AI version of yourself. Please feel free to reach out if you need help in any form.

1. Get help (besides office hours)

  • Dropbox: The lecture pdfs will be uploaded to Dropbox (follow the link) and you can ask questions there by making comments on the slides directly.
  • Support: The university counseling services center provides a variety of programs and activities.
  • Accommodations for students with disabilities: If you are a student with a documented disability seeking reasonable accommodations in this course, please contact Kathy Duggan, (617) 552-8093, dugganka@bc.edu, at the Connors Family Learning Center regarding learning disabilities and ADHD, or Rory Stein, (617) 552-3470, steinr@bc.edu, in the Disability Services Office regarding all other types of disabilities, including temporary disabilities. Advance notice and appropriate documentation are required for accommodations.

2. Homework submission

All programming assignments are in Python on Colab, always due at midnight (11:59 pm) on the due date .
  • Install Colab on the browser: Sign in to your Google account, follow the [link] to the folder of assignments, click on *.ipynb, click on "Open with" and "Connect more apps", install "Colaboratory".
  • Submission: You need save a copy of the file in your own Google drive, so that you can save your edits. Afterwards, you can download the ipynb file and submit it to Canvas.
  • Lab (1 per week): Every lecture has a lab exercise to help you gain the hands-on understanding about the material. The lab on previous week's lectures is due on Wednesday. We will go through some code in class and you need to finish up the exercises.
  • Pset (4 in total): In each pset, we will build a working prototype for a biology lab or a healthcare startup.
  • Final project: (guideline) In lieu of a final exam, we'll have a final project. This project will be completed in small groups during the last weeks of the class. The direction for this project is open-ended: you can either choose from a list of project ideas that we distribute, or you can propose a topic of your own. A short project proposal will be due approximately halfway through the course. During the final exam period, you'll turn in a final report and give a short presentation. You may use an ongoing research work for your final project, as long it meets the requirements.

3. Academic policy

  • Late days: You'll have 10 late days each for labs and psets respectively over the course of the semester. Each time you use one, you may submit a homework assignment one day late without penalty. You are allowed to use multiple late days on a single assignment. For example, you can use all of your days at once to turn in one assignment a week late. You do not need to notify us when you use a late day; we'll deduct it automatically. If you run out of late days and still submit late, your assignment will be penalized at a rate of 10% per day. If you edit your assignment after the deadline, this will count as a late submission, and we'll use the revision time as the date of submission (after a short grace period of a few minutes). We will not provide additional late time, except under exceptional circumstances, and for these we'll require documentation (e.g., a doctor's note). Please note that the late days are provided to help you deal with minor setbacks, such as routine illness or injury, paper deadlines, interviews, and computer problems; these do not generally qualify for an additional extension.
  • Academic integrity: While you are encouraged to discuss homework assignments with other students, your programming work must be completed individually. You may not search for solutions online, or to use existing implementations of the algorithms in the assignments. Thus it is acceptable to learn from another student the general idea for writing program code to perform a particular task, or the technique for solving a mathematical problem, but unacceptable for two students to prepare their assignments together and submit what are essentially two copies of identical work. If you have any uncertainty about the application of this policy, please check with me. Failure to comply with these guidelines will be considered a violation of the University policies on academic integrity. Please make sure that you are familiar with these policies. We will use moss.pl tool to check each lab and pset for plagriasm detection.

4. Additional resource
Acknowledgements: This course is a biomedical version of CSCI 3343: Computer Vision with concrete biomedical image analysis applications. See the "Acknowledge" section in that class.

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