Topic: colon-cancer Goto Github
Some thing interesting about colon-cancer
Some thing interesting about colon-cancer
colon-cancer,Colorectal cancer (CRC) is the second most dangerous type of cancer in terms of causing deaths in patients and third most common type of cancer found in people in terms of incidence. CRC can be further categorized based on its molecular subtypes. Each subtype displays different features. Thus, identifying molecular subtypes of CRC and treating patients accordingly can help achieve better therapeutic results rather than providing the same treatment for all colorectal cancer patients. Our research aims at identifying molecular subtypes of colorectal cancer using gene expression data by building a model using some machine learning and deep learning algorithms.
User: aastha1840
colon-cancer,Rasa Gastroenterologist AI Chatbot to help doctor detect patients colon cancer lesions using Unity, Darknet Yolo, Keras CNN
Organization: ai-medical-robotics
colon-cancer,Exploring the Supervised Learning Models to Automatically Diagnose Colon Cancer Patients based on their SNP Profiles
User: alibrahimzada
colon-cancer,GitHub repository for Medico automatic polyp segmentation challenge
User: debeshjha
Home Page: https://multimediaeval.github.io/editions/2020/tasks/medico/
colon-cancer,Kvasir-SEG: A Segmented Polyp Dataset
User: debeshjha
Home Page: https://datasets.simula.no/kvasir-seg/
colon-cancer,A multi-centre polyp detection and segmentation dataset for generalisability assessment https://www.nature.com/articles/s41597-023-01981-y
User: debeshjha
Home Page: https://drive.google.com/drive/folders/16uL9n84SrMt7IiQFzTUQNaJ9TbHJ8DhW?usp=sharing
colon-cancer,colorectal cancer
User: gari3008ma
colon-cancer,Repo which includes the medical data sets used in a feature selection paper proposed by OASYS group
Organization: groupoasys
Home Page: https://drive.google.com/drive/folders/1ytXwG8pbJvPD6MgFHKEZfZTh4Ix3kiBm
colon-cancer,Image classification on lung and colon cancer histopathological images through Capsule Networks or CapsNets.
User: koushikkumarl
colon-cancer,TRAL pipeline for tandem repeat detection in proteins. Specifically in such which are related to colorectal cancer.
User: matteodelucchi
colon-cancer,Detection of Colon Cancer Cell and its types using a semi-supervised approach with deep learning
User: nischaybikramthapa
colon-cancer,KBSMC colon cancer grading dataset repository
Organization: quiil
colon-cancer,KBSMC_colon_tma_cancer_grading_1024_dataset
Organization: quiil
colon-cancer,A collection of small-sample, high-dimensional microarray data sets to assess machine-learning algorithms and models.
User: ramhiser
colon-cancer,Codes for parameter estimation and sensitivity analysis of QSP models for colon cancer. This is a part of the National Cancer Institute funded project titled "Data-driven QSP software for personalized colon cancer treatment" Achyuth Manoj, Susanth Kakarla, Suvra Pal and Souvik Roy.
User: roysouvik2
colon-cancer,Image classifier for colon cancer detection from colonoscopies.
User: shiv213
colon-cancer, Colorectal cancer (CRC) is the second most dangerous type of cancer in terms of causing deaths in patients and third most common type of cancer found in people in terms of incidence. CRC can be further categorized based on its molecular subtypes. Each subtype displays different features. Thus, identifying molecular subtypes of CRC and treating patients accordingly can help achieve better therapeutic results rather than providing the same treatment for all colorectal cancer patients. Our research aims at identifying molecular subtypes of colorectal cancer using gene expression data by building a model using some machine learning and deep learning algorithms.
User: shivangi1raghav
colon-cancer,Developed a fine-tuned EfficientNetB0 model which is a pre-trained Convolutional Neural Network (CNN) model to train using lungs and colon cancer dataset and classify if the unseen image belonged to benign, adenocarcinoma or squamous cell carcinoma cancer type.
User: techysanjo
colon-cancer,Find patients who have concerning tests but no timely follow-up
User: zimolzak
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