DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

By Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers. Journal of Medical Imaging, 2018

562 citations recorded by the source corpus.

Abstract. Extracting, harvesting, and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations have been collected and stored in hospitals’ picture archiving and communication systems (PACS). These types of annotations, also known as bookmarks in PACS, are usually marked by radiologists during their daily workflow to highlight significant image findings that may serve as reference for later studies. We propose to mine and harvest these abundant retrospective medical data to build a large-scale lesion image dataset. Our process is scalable and requires minimum manual annotation effort. We mine bookmarks in our institute to develop DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications. Using DeepLesion, we train a universal lesion detector that can find all types of lesions with one unified framework. In this challenging task, the proposed lesion detector achieves a sensitivity of 81.1% with five false positives per image.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/98a68e8148d380120924d5c2810e3e0f513a75b2

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In Artificial Intelligence

In Pulmonary and Respiratory Medicine

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Citations in the corpus

Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, background): “Alternatively, a few studies have successfully used automated mining of medical image databases such as hospital picture archiving and communication systems (PACS) to build large training datasets [22], [23].”
Preparing Medical Imaging Data for Machine Learning (2020, methodology): “Data sets are available in a wide range of domains from neuroimaging (72–77), breast imaging (36,78), chest radiographs (41,79), knee MRI (80), body CT (60), and others; a list of well-known open-source data sets is given in Table 2.”
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI (2018, background): “Datasets such as BraTS [24], LUNA [37], ChestX-ray [50], DeepLesion [55], and Camelyon [1] are widely used in research with clinical applications.”
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023, background): “, organ segmentation [91, 22, 68, 7] or tumor detection [8, 79, 81].”
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation (2019, unknown): “comparable to Yang et al. (2017), but are obtained using datasets that are two orders of magnitude larger in size.”
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks (2019, methodology): “For the CycleGAN-training images from the DeepLesion data set 15 were used. experimental setup.”
Continual Self-Supervised Learning: Towards Universal Multi-Modal Medical Data Representation Learning (2023, methodology): “CT scans are obtained from the DeepLesion dataset [49].”