Read ImmunoHistoChimic images, segmented cells (pre-trained stardist model) and classifyed
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Updated
Oct 15, 2023 - Python
Read ImmunoHistoChimic images, segmented cells (pre-trained stardist model) and classifyed
Project focuses on diagnosing cancer through image analysis. It utilizes machine learning models and techniques to analyze medical images and classify cancerous cells or tumors. It aims to improve cancer diagnosis accuracy and assist healthcare professionals.
Dissertation on Cancer Detection [Prostate Cancer] Research and Study
This is the home for deployment scripts used to setup the Radiomics platform. This site was published at data.radiomics.io and maintained by @Kitware.
Per-Pixel Recognition of Cancers using Oriented Gabor filter on the GPU
A machine-learning model that uses a convolutional neural network to classify lung tumors in CT scans, which will help detect lung tumors that might have went unnoticed
Skin cancer detection using deep learning model vgg-16 in pytorch. Gradcam allows to visualize discriminating features
Python implementation of topology descriptors which capture subtle sharpness and curvature differences along the surface of diseased pathologies on imaging.
Detecting various characteristics of glioblastoma using Deep Learning
Bayesian Non-Parametric Image Segmentation using HDP-MRF
Skin cancer classification using transfer learning
Predict survival time from PET scans
Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]
Probabilistic topic model for identifying cellular micro-environments.
Python Implementation of the CoLlAGe radiomics descriptor. CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.
Open source of Pyradiomics extension
AI-based pathology predicts origins for cancers of unknown primary - Nature
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