Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning- Model weights

Abstract

<p> </p> <h3><strong>Model Documentation: Multidisease Classification Models for Body CT Scans</strong></h3> <p>This document provides an overview and usage guidance for three deep learning models developed to perform multidisease classification on body CT scans. The models are based on 3D convolutional neural networks implemented in <strong>Python using TensorFlow</strong>, and they were trained using weak supervision derived from radiology report text.</p> <h4><strong>Background and Purpose</strong></h4> <p>These models were developed as part of a retrospective study aiming to detect multiple common disease conditions across three major organ systems—lungs and pleura, liver and gallbladder, and kidneys and ureters—using body CT scans. Labels for training were extracted using rule-based natural language processing (NLP) from radiology reports, enabling efficient training without extensive manual annotation.</p> <p>The work demonstrates how weak supervision can support the development of clinically relevant, multi-organ disease classifiers on a large scale.</p> <h4><strong>Model Summary</strong></h4> <p>Each model targets a specific organ system and predicts the presence or absence of five disease categories (four pathologies + one "no apparent disease" class):</p> <ol> <li> <p><strong>Lungs and Pleura: </strong></p> <ul> <li> <p><strong>Labels</strong>: Atelectasis, Nodule, Emphysema, Effusion, No Apparent Disease</p> </li> <li> <p><strong>Performance (AUCs)</strong>:</p> <ul> <li> <p>Atelectasis: 0.77</p> </li> <li> <p>Nodule: 0.65</p> </li> <li> <p>Emphysema: 0.89</p> </li> <li> <p>Effusion: 0.97</p> </li> <li> <p>No Apparent Disease: 0.89</p> </li> </ul> </li> </ul> </li> <li> <p><strong>Liver and Gallbladder</strong></p> <ul> <li> <p><strong>Labels</strong>: Hepatobiliary Calcification, Lesion, Dilation, Fatty Liver, No Apparent Disease</p> </li> <li> <p><strong>Performance (AUCs)</strong>:</p> <ul> <li> <p>Calcification: 0.62</p> </li> <li> <p>Lesion: 0.73</p> </li> <li> <p>Dilation: 0.87</p> </li> <li> <p>Fatty: 0.89</p> </li> <li> <p>No Apparent Disease: 0.82</p> </li> </ul> </li> </ul> </li> <li> <p><strong>Kidneys and Ureters</strong></p> <ul> <li> <p><strong>Labels</strong>: Stone, Atrophy, Lesion, Cyst, No Apparent Disease</p> </li> <li> <p><strong>Performance (AUCs)</strong>:</p> <ul> <li> <p>Stone: 0.83</p> </li> <li> <p>Atrophy: 0.92</p> </li> <li> <p>Lesion: 0.68</p> </li> <li> <p>Cyst: 0.70</p> </li> <li> <p>No Apparent Disease: 0.79</p> </li> </ul> </li> </ul> </li> </ol> <p>The models were trained on CT data from over 13,000 scans and evaluated on a subset of 2,158 volumes with 2,875 manually validated reference labels. Automated label extraction achieved between 91%–99% accuracy during internal validation.</p> <h4><strong>Implementation Details</strong></h4> <ul> <li> <p><strong>Programming Language</strong>: Python</p> </li> <li> <p><strong>Framework</strong>: TensorFlow</p> </li> <li> <p><strong>Model Type</strong>: 3D Convolutional Neural Network (CNN)</p> </li> <li> <p><strong>Preprocessing</strong>: Organ segmentation (via DenseVNet), intensity normalization, and cropping of CT volumes to organ-specific regions of interest.</p> </li> </ul> <h4><strong>Repository Links</strong></h4> <p>The source code, model weights, and usage instructions will be made publicly available through:</p> <ul> <li> <p><strong>GitHub Repository</strong>: https://github.com/fitushar/multi-label-weakly-supervised-classification-of-body-ct</p> </li> <li> <p><strong>GitLab Repository</strong>: https://gitlab.oit.duke.edu/railabs/LoGroup/multi-label-weakly-supervised-classification-of-body-ct</p> </li> </ul> <p>These repositories include:</p> <ul> <li> <p>Model loading and inference scripts</p> </li> <li> <p>Preprocessing pipeline details</p> </li> <li> <p>Instructions for applying the model to new CT data</p> </li> <li> <p>Evaluation tools and AUC reporting scripts</p> </li> </ul> <h4><strong>License and Citation</strong></h4> <p>These models are released for academic research purposes only. If you use them in your work, please cite the original study. Citation details will be provided in the repository README.</p>

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