glemaitre Goto Github PK

2020-scikit-learn-tutorial

acme

ACME - Accelerated Model Explainability

admin-requests

administrative

Project management related documents for scikit-learn

air_passengers

Air passengers RAMP starting kit

ampute

Small project to explore different types of amputation

annoy

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

apeirelec-ws

ascfours-web

Site internet de l'ASC Fours

asm_pca

autism

RAMP on Autism Spectrum Disorder detection

b31xb-iip-tp-1718

basic-node

Continuous integration for a basic Node.js project with Shippable https://www.shippable.com

beamer-le2i

Beamer for LE2I

bench_lobpcg

bike_counters

Bike counters RAMP starting kit

blog

Hosting the scikit-learn blog.

bm3d

Clone from http://www.cs.tut.fi/~foi/GCF-BM3D/index.html#ref_software

book-chapter-thesis

book_cedric

boston_housing

Boston housing price regression

bow-sklearn-example

brew

brew: Python Ensemble Learning API

bscv1617sift

bscvdemocpp1718

c-lasso

c-lasso: a Python package for constrained sparse regression and classification

ca_09_2020

cads-prostate-survey

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

camcan_analysis

Analyzing fMRI and behavioral data from Cam-CAN dataset.

ccub-presentation