Machine Learning Projects

This library contains various scripts useful for running machine learning projects, including performing hyperparameter optimization and k-fold cross validation while storing results with WandB. Some work is currently under development.

Author's Note: Some projects associated with the Master's Thesis are published on Google Scholar. https://scholar.google.com/citations?user=SJCymuoAAAAJ&hl=en

Author Profile: https://www.linkedin.com/in/anish-s-36179a97/

Sweep Template for K-Fold Cross Validation & Hyperparameter Optimization with WandB

KCV_HP_Template: A template script which can perform k-fold cross validation & hyperparameter optimization

MS Thesis: Machine Learning for Abdominal Aortic Aneurysm Characterization from Standard-Of-Care Computed Tomography Angiography Images

Masters_Thesis: Notebooks associated with the completion of the Master's Thesis

1. AAA-UNet: Baseline Aneurysm Segmentation

2. BB-AAA-UNet: Memory Efficient High-Resolution Segmentation with Prior Aneurysm Localization

3. BB-AAA-UNet: As Applied to Aneurysm Wall Segmentation

4. Patch Segmentation UNet: Prediction of Aneurysm Wall by Medical Image Sub-volumes

5. AAA Image Transformers: Classifying Medical Images by Aneurysm Severity with Latent Representations

6. AAA-ViT: Moving Towards Detection with Classification of Aneurysm Severity with Anatomical Explanation

Peripheral Artery Disease Classification from Computed Tomography Angiography Images via 3D Medical Image Vision Transformers with Explainability

PAD_ViT: Repository of a medical image classification project for ImageRx.

Any Segmentation Model: The 3D Foundational Segmentation Model to Revolutionize Medical Image Annotation

ASM: An example use case of applying the 3D Foundational Model to segment volumetric data. This model was outfitted with a text encoder. Based on: https://github.com/facebookresearch/segment-anything

Self-Supervised Medical Image Classification of Radiographs via Convolutional Neural Network Inpainting and Class Balanced Loss Functions

Self_Supervised_Learning: An example of self-supervised learning in action.

Visual Question Answering of Colonoscopy Medical Images

MEDVQA: A vision language model.

Utilizing RetinaNet for Automatic Face Mask Detection and Real-Time Camera Performance

RetinaNet: An automatic face mask detector which uses bounding boxes. The script is the one stop shop for data curation, model development, statistical benchmarking, and deployment on a local computer.

Machine Learning & Image Processing Coding Interview Prompts

Coding_Questions: A folder of various quick and simple machine learning scripts for practice.