AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

By Pranav Rajpurkar, Allison Park, Jeremy Irvin, Chris Chute, Michael D. Bereket, Domenico Mastrodicasa, Curtis P. Langlotz, Matthew P. Lungren, Andrew Y. Ng, B. Patel. 2020

112 citations recorded by the source corpus.

The development of deep learning algorithms for complex tasks in digital medicine has relied on the availability of large labeled training datasets, usually containing hundreds of thousands of examples. The purpose of this study was to develop a 3D deep learning model, AppendiXNet, to detect appendicitis, one of the most common life-threatening abdominal emergencies, using a small training dataset of less than 500 training CT exams. We explored whether pretraining the model on a large collection of natural videos would improve the performance of the model over training the model from scratch. AppendiXNet was pretrained on a large collection of YouTube videos called Kinetics, consisting of approximately 500,000 video clips and annotated for one of 600 human action classes, and then fine-tuned on a small dataset of 438 CT scans annotated for appendicitis. We found that pretraining the 3D model on natural videos significantly improved the performance of the model from an AUC of 0.724 (95% CI 0.625, 0.823) to 0.810 (95% CI 0.725, 0.895). The application of deep learning to detect abnormalities on CT examinations using video pretraining could generalize effectively to other challenging cross-sectional medical imaging tasks when training data is limited.

Subfields: Computer Vision and Pattern Recognition, Emergency Medicine, Oncology, Radiology, Nuclear Medicine and Imaging, Medical Imaging

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Citations in the corpus

ScaleNet: An Unsupervised Representation Learning Method for Limited Information (2023, background): “However, collecting and labeling the large-scale training dataset is a very time-consuming and expensive task in fields such as neuroscience [42], medical diagnosis [40], material science [9], and chemistry application [17].”
Harnessing multimodal data integration to advance precision oncology (2021, background): “Three-dimensional CNNs have shown success in stratifying patients with non- small-cell lung cancer (NSCLC) by overall survival (OS) 26 , and empirically outperformed two-dimensional CNNs in other radiology tasks, such as diagnosing appendicitis 27 .”
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation (2021, background): “In closing, our work demonstrates the potential beneﬁts of incorporating patient meta-data into self-supervised contrastive learning for medical images, and can be extended to a broader set of tasks (Rajpurkar et al., 2020; Uyumazturk et al., 2019).”
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2021, unknown): “This survey adds to the review of Reyes et al. (2020); since they mainly discussed techniques in computer vision, without extensively evaluating the adaptation of such techniques throughout medical image analysis.”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “Appendicitis can be detected on CT scans by pretraining with natural world videos [205].”