Code accompanying our ICVGIP 2016 paper

Implementation of a classification algorithm which accurately identifies cervix type based on images for Kaggle challenge using Keras

Skin cancer classification using transfer learning

Per-Pixel Recognition of Cancers using Oriented Gabor filter on the GPU

Visualize similar cells on a 2D plot using t-SNE

Track 2D cell motion and mitosis in time-lapse microscopy

Classification of Clear Cell Renal Cell Carcinoma using CT textural feature analysis

Bayesian Non-Parametric Image Segmentation using HDP-MRF

Skin cancer detection using deep learning model vgg-16 in pytorch. Gradcam allows to visualize discriminating features

Worked with Dr. Shandong Wu at University of Pittsburgh to use software to improve outcomes for breast cancer risk detection.

This repository contains skin cancer lesion detection models. These are trained on a sequential and a custom ResNet model

Predict survival time from PET scans

In this project, we deploy the Bayesian Convolution Neural Networks (BCNN), proposed by Gal and Ghahramani [2015] to classify microscopic images of blood samples (lymphocyte cells). The data contains 260 microscopic images of cancerous and non-cancerous lymphocyte cells. We experiment with different network structures to obtain the model that re…

"The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer‐aided detection" by Vicente-Munuera et al.

Dissertation on Cancer Detection [Prostate Cancer] Research and Study

3D Slicer Extension Implementation the CoLlAGe radiomics descriptor

📎 About MIDA Project

Convolution Neural Network to predict Skin cancer. Skin cancer is considered as one of the most dangerous types of cancers and there is a drastic increase in the rate of deaths due to lack of knowledge on the symptoms and their prevention. Thus, early detection at premature stage is necessary so that one can prevent the spreading of cancer. Skin…

DCE MRI analysis in Julia