Segment Anything in Medical Images

lidc nodule detection with CNN and LSTM network

Code repository for the online course Machine Learning with Imbalanced Data

Repository supporting the original research paper in Nature Communications (Primakov et al. 2022)

Weakly-supervised learning pipeline for histopathology images. Publications: Biomarker prediction in colorectal cancer (CRC)

NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline

Code accompanying the paper "Multimodal fusion of imaging and genomics for lung cancer recurrence prediction" - Vaishnavi Subramanian, Minh N. Do, Tanveer Syeda-Mahmood (ISBI 2020)

ViT Attention map visualization (using Custom ViT and Pytorch timm module)

Stock Prediction using LSTM, Linear Regression, ARIMA and GARCH models. Hyperparameter Optimization using Optuna framework for LSTM variants.

Bioinformatics Project. Trying to find out the most influential metabolomic biomarkers(from 158) for predicting Lung Cancer. Also, trying to make some predictions about the disease.

[MICCAI'24 DEMI Workshop - Best Paper Award] Evaluating Histopathology Foundation Models for Few-shot Tissue Clustering: an Application to LC25000 Augmented Dataset Cleaning

Unofficial Instructions for downloading TCIA-CPTAC Pathology Images Lung Cohorts: LUAD, LSCC (aka LUSC)

Handled Highly Imbalanced Data

This file contains the code for my work. "Reference5" is the image used for normalization. The "stained_dataset_test" is the final result of the model. The "lc25000_dataset_test" is the result of m…

Road Image Segmentation with UNet and PyTorch

Classification of lung and colon cancer from histopathological images (LC25000) using EfficientNetV2.

A predictive model to predict survival/mortality of lung-cancer patients one year after thoracic surgery

Medical Imaging Segmentation

Lung Cancer Survival Prediction Module

I included 7 sample lung pathology images (Lung Adenocarcinoma, Squamos Cell Carcinoma, and Mesothelioma). Attached are folders containing each respective samples and specific normalization results.