Name: Chetan Srinidhi
Type: User
Company: Sunnybrook Research Institute, Medical Biophysics, University of Toronto
Bio: Medical Image Analysis, Computational Pathology, Machine Learning, Computer Vision
Twitter: ChetanSrinidhi
Location: Toronto, Canada
Blog: https://srinidhipy.github.io/
Chetan Srinidhi's Projects
A beautiful, simple, clean, and responsive Jekyll theme for academics
pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"
CoNIC Challenge
Keras implementation of Representation Learning with Contrastive Predictive Coding
Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, C++ and more.
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22]
Codes for our MIA paper "DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks".
Scaling and Benchmarking Self-Supervised Visual Representation Learning
PyTorch extensions for high performance and large scale training.
A library for efficient similarity search and clustering of dense vectors.
This repository contains the material we prepared for a short hands-on course for introducing Git and GitHub to the participants.
A fast hierarchical dimensionality reduction algorithm.
Histocartography is a framework bringing together AI and Digital Pathology
Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.
This repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.
Official code for "Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology", ICCV, 2021, CDpath Workshop (Oral).
pytorch Implementation of U-Net, R2U-Net, Attention U-Net, Attention R2U-Net.
This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
Tools for computational pathology
Quality control of whole-slide images through multi-class semantic segmentation of artifacts
pipreqs - Generate pip requirements.txt file based on imports of any project. Looking for maintainers to move this project forward.
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Simple PyTorch implementations of U-Net/FullyConvNet (FCN) for image segmentation
RadImageNet, a pre-trained convolutional neural networks trained solely from medical imaging to be used as the basis of transfer learning for medical imaging applications.