Medical Image Classification

LIDC-IDRI

• The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

• Lung Image Database Consortium (LIDC) Nodule Size Report

• Highly accurate model for prediction of lung nodule malignancy with CT scans

Bookmarks

• Review of CAD for lung cancer
  • meet the following requirements:
    • improve the performance of radiologists providing high sensitivity in the diagnosis
    • a low number of false positives (FP)
    • high processing speed
    • present high level of automation
    • low cost (of implementation, training, support and maintenance)
    • the ability to detect different types and shapes of nodules
    • software security assurance

https://towardsdatascience.com/computer-vision-feature-extraction-101-on-medical-images-part-1-edge-detection-sharpening-42ab8ef0a7cd

• Pyradiomics
• Top placing

Evaluation

https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/ https://www.ritchieng.com/machine-learning-evaluate-classification-model/

Training Model

Image Classification using Python and Scikit-learn Python machine learning: Introduction to image classification

Online Courses

• Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition
• MIT: Biomedical Signal and Image Processing

• Udacity

• https://blog.paralleldots.com/data-science/research-papers-image-classification/

SPIE-AAPM Lung CT Challenge

classification

quantitative image analysis methods for the diagnostic classification of malignant and benign lung nodules

• TCIA Dataset
• Readings:
  1. Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned
  2. LUNGx Challenge for computerized lung nodule classification
• Google Cloud Citations

Collection Statistics
Modalities	CT
Number of Patients	70
Number of Studies	70
Number of Series	70
Number of Images	22,489
Images Size (GB)	12.1

• training set (10 subjects) - CT-Training
• test set (60 subjects) - LUNGx

LUng Nodule Analysis 2016 (LUNA16)

detection

1. Nodule detection (NDET) Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location. The pipeline typically consists of two stages: candidate detection and false positive reduction.
2. False positive reduction (FPRED) Given a set of candidate locations, the goal is to assign a probability for being a nodule to each candidate location. Hence, one could see this as a classification task: nodule or not a nodule. Candidate locations will be provided in world coordinates. This set detects 1,162/1,186 nodules.

• Dataset

• Preparation to Data Science Bowl 2017

• tutorial

Data Science Bowl 2017

classification

determine when lesions in the lungs are cancerous

• Kaggle Dataset
• Discussion: Congratulations to our Top 3 Kernels Prize Winners!
• 2nd place solution for the 2017 national datascience bowl
• lung cancer - Full Preprocessing Tutorial

Lung CT Segmentation Challenge 2017 (LCTSC)

detection

comparison of various auto-segmentation algorithms

• TCIA Dataset
• Google Cloud Citation

Collection Statistics
Modalities	CT, RT
Number of Patients	60
Number of Studies	60
Number of Series	96
Number of Images	9,569
Images Size (GB)	4.8

LungCT-Diagnosis

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detection, prediction

extract prognostic image features that will describe lung adenocarcinomas and will associate with overall survival

• TCIA Dataset
• Google Cloud Citation

Collection Statistics
Modalities	CT
Number of Patients	61
Number of Studies	61
Number of Series	61
Number of Images	4682
Images Size (GB)	2.5

Dataset

Data downloaded from cancerimagingarchive.net.

sudo apt-get install icedtea-netx

javaws /path/to/your.jnlp

Breast Cancer

https://www.kaggle.com/junkal/breast-cancer-prediction-using-machine-learning https://medium.com/@Petuum/deep-learning-for-breast-cancer-identification-from-histopathological-images-f38de0a658a5