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acute-lymphoblastic-leukemia-cell-classification-using-bayesian-convolutional-neural-networks

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 return lowest error rate in classifying the images. We estimate the uncertainty for the predictions made by the models which in turn can assist a doctor in better decision making. The Stochastic Regularization Technique (SRT), popularly known as Dropout is utilized in the BCNN structure to obtain the Bayesian interpretation.

aml-all-classifiers

The Peter Moss Acute Myeloid/Lymphoblastic Leukemia classifiers are a collection of projects that use computer vision to classify Acute Myeloid/Lymphoblastic Leukemia in unseen images. The projects include classifiers made with Tensorflow, Caffe, Intel Movidius (NCS & NCS2), OpenVino and pure Python classifiers.

andrew-ng-deep-learning-notes

吴恩达《深度学习》系列课程笔记及代码

anemia-detection-by-deep-learning

This is a modification of the previous work by using deep neural networks and it aims to detect 10 different cell types in Iron deficiency anemia, Thallasemia minor and Sickle cell anemia.

atten_deep_mil

This is an implementation of ICML 2018 "Attention-based Deep MIL"

attentiondeepmil

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

attfpn-ovarian-cancer

awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

awesome_malaria_parasite_imaging_datasets

This repository is an attempt to list all malaria parasite imaging datasets (blood smears). Contributions welcome!

bccd_dataset

BCCD Dataset is a small-scale dataset for blood cells detection.

blog

为我的博客写材料，并且学习，都是一些代码或者资料

blood_cells

Counting Red and White Blood Cells from Images

books

useful books on deep learning or machine learning

breast_cancer_classifier

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

cascade-rcnn

Caffe implementation of multiple popular object detection frameworks

cc-cruiser

cellprofiler

An open-source application for biological image analysis

ceng783_project

chinese-ai-developer

👩🏿‍💻👨🏾‍💻👩🏼‍💻👨🏽‍💻👩🏻‍💻** AI 开发者项目列表 -- 分享大家都在做什么

cl2227619761.github.io

clam

Data Efficient & Weakly Supervised Computational Pathology on Whole Slide Images

cnn_finetune

Fine-tune CNN in Keras

convnets-keras

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

datasciencectacontent

repository for Community Mentor content related to the Johns Hopkins University Data Science Specialization on Coursera

deep-learning-for-medical-applications

Deep Learning Papers on Medical Image Analysis

deep-learning-models

Keras code and weights files for popular deep learning models.

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deep-mil-for-whole-mammogram-classification

Zhu, Wentao, Qi Lou, Yeeleng Scott Vang, and Xiaohui Xie. "Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification." MICCAI 2017.