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On TCIA you can find these data in a couple of ways.  

1. When Browsing Collections
   1. You
   For Collections datasets you
   1. can look for SEG / RTSTRUCT in the modality column to determine where DICOM segmentations or contours are available. 
   2. You can also filter for "Image Analyses" in the supporting data column.  If a collection says "Image Analyses" but does not include SEG or RTSTRUCT in the modality this is typically because the analysis was in some other format.  This could be segmentation data in NIFTI/NRRD/MHA formats, but it might also represent some other kind of analysis such as image classification.
2. For When Browsing Analysis Results of existing derived from TCIA collections it is a bit more straightforward.  Simply , simply use the filter above the table to search for "segmentations" which will find any instance of this in the Analysis Artifacts column.

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TCIA includes a wealth of non-image data which could be utilized for image classification purposes.  

1. Clinical data (e.g. outcomes, stage) - Filter the table for "clinical" on the Browse Collections page to find datasets with this type of information
2. Distinguishing between cancer types (lgg vs gbm)e.g. low grade vs high grade gliomas) - Cancer Type is one of the columns on the Browse Collections, making it easy to filter or search for datasets based on this criteria.
3. Genomic/Proteomic subtypes - Filter the table for "genomics" or "proteomics" on the Browse Collections page to find datasets with this type of information.  In most cases you will need to retrieve specific details about the patients' genomic/proteomic from external databases such as NCI's Genomic Data Commons or Proteomic Data Commons.  Please note these websites are not supported by TCIA staff, but we do coordinate with the teams that operate these archives to ensure common patient identifiers are used which enable you to link these data to TCIA images.

Suggested Deep Learning Parameters for TCIA Result Submission

Deep Learning parameters are critical for researchers to reproduce Deep Learning experiments. However, it is usually the author's discretion for the completeness and format of reported parameters in manuscripts. This document proposes a list of essential Deep Learning parameters to be included in TCIA results submission process. The goal is to explicitly capture these parameters in a common format Where applicable, we recommend that data submitters include the following key pieces of information in their dataset summaries such that TCIA users can easily reproduce their study and compare their analysis results from TCIA data.

List of Deep Learning Parameters

1. Deep Neural Network (DNN) Name - for example, VGG16, ResNet-101, UNet, etc., or a link to GitHub repository or manuscript for customized DNNs if applicable.

2. Data Augmentation Methods - for example, color augmentation (HSV or RGB color space), transformation, noise, GAN, patch generation, downsizing parameters, etc.

3. Training, Validation, and Testing Set Configuration - for example number of samples per each set, total number of samples, etc.

4. Hyperparameters - for example, learning rate, early stopping, batch size, number of epochs, etc.

5. Training Statistics - for example, wall time spent in training, accuracy metrics such as if average score or best score is reported, etc.

6. Training Environment - for example, GPU type, Deep Learning framework used such as TensorFlow/PyTorch, number of GPUs, number of nodes, etc

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Third party tips and tutorials for applying deep learning to medical imaging data

1. https://www.youtube.com/watch?v=-XUKq3B4sdw - how a radiologist interprets lung CTs?
2. https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial - how to pre-process images for deep learning
3. https://theaisummer.com/medical-image-coordinates/ - DICOM deep learning for medical imaging novices
4. https://developer.nvidia.com/clara-medical-imaging - NVIDIA package for simplifying deep learning tasks in medical imaging
5. https://forums.fast.ai/t/fastai-v2-has-a-medical-imaging-submodule/56117 - FastAI package for simplifying deep learning in medical imaging
6. "TCIA as a Centralized Data Resource for Development of AI" from RSNA 2019
7. https://www.kaggle.com/marcovasquez/basic-eda-data-visualization - RSNA intracranial hemorrhaging guide  
8. https://github.com/RSNA/AI-Deep-Learning-Lab - RSNA 2019 deep learning course
9. https://github.com/RSNA/MagiciansCorner - Notebooks, datasets, other content for the Radiology:AI series known as Magicians Corner by Brad Erickson