Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

By J. Shiraishi, S. Katsuragawa, J. Ikezoe, Tsuneo Matsumoto, Takeshi Kobayashi, K. Komatsu, Mitate Matsui, Hiroshi Fujita, Y. Kodera, Kunio Doi. AJR. American journal of roentgenology, 2000

1,094 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/2b3005cd5016f5456af2c7229e86c47139226089

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In Artificial Intelligence

In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “[48] 2D X-Ray JSRT dataset [42] pr Elastic deformations”
PadChest: A large chest x-ray image dataset with multi-label annotated reports (2019, methodology): “The JSRT dataset [17] consists of 247 x-rays of which 154 have been labeled for lung nodules (100 malignant).”
Transformers in Medical Imaging: A Survey (2022, methodology): “Dataset Name Dataset Size Modality RSNA [264] 29,700 X-ray JSRT [194] 247 X-ray BraTS 2018 [398] 1689 MRI BraTS 2019 [185] 1675 MRI OASIS [399] 373-2168 MRI HAM10000 [272] 10,000 Dermatoscopic ISIC 18 [101] 3594 Dermatoscopic LUNA 16 [400] 888 CT-Scans NIH Chest X-ray [196] 112,000 X-ray APTOS [255]”
Computer-aided diagnosis in chest radiography: a survey (2001, background): “The volume is estimated using an empiric formula that assumes simple shapes for the lungs and that has been shown to correlate well with the true TLC. Paul et al. [103] described a method to automatically determine the TLC using profile analysis similar to Kruger et al. [101].”
Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration (2013, positive): “In 2006, the same research group [15] delineated lung, heart, and clavicle boundaries of the JSRT set [13] under the supervision of a radiologist.”
Segment Anything in Medical Images (2023, background): “…T2, ADC, FLAIR) [24] [12], breast tumor, liver, and vessel segmentation in ultrasound images [2][48], heart and lungs segmentation in X-Ray images [40], polyp and instrument segmentation in endoscope images [3][16], vessel segmentation in retinal images [1], and colon gland segmentation in…”
TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation (2021, methodology): “To verify the effectiveness and efﬁciency of our TransAt-tUnet, we ﬁrst conducted comparative experiments for the task of skin lesion segmentation on ISIC-2018 [31] dataset, and lung ﬁeld segmentation on the combination of the JSRT [32], Montgomery [33], and NIH [23] datasets.”
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks (2017, positive): “Detection of car-diomegaly is done only for the frontal CXR images from the Indiana Dataset.”
Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization (2021, background): “CXR14 [90] JSRT [82]are the in-domain data set, on which we both train and evaluate.”
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs (2019, methodology): “The Japanese Society of Radiological Technology (JSRT) Database contains 247 images with labels of chest nodules as conﬁrmed by subsequent computed tomography (CT) [9].”
Computer-aided detection in chest radiography based on artificial intelligence: a survey (2018, methodology): “JSRT dataset [21, 22] The dataset was compiled by the Japanese Society of Radiological Technology (JSRT) and includes 247 chest radiographs, of which 154 have pulmonary nodules (100 malignant and 54 benign), and 93 have no nodules.”
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018, methodology): “For example, the JSRT dataset [24], [25] contains 247 chest X-ray images including 154 lung nodules.”
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations (2022, methodology): “We construct the chest X-ray segmentation dataset by combining JSRT/SCR [66], NLM [35], Shenzhen [35] and NIH [68] datasets and split it into 735 train and 316 test images.”