IBM releases chest x-ray dataset for algorithm training

AuntMinnie.com staff writers

Mar 30, 2021

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IBM has created a new multimodal chest x-ray dataset designed to assist in the training of artificial intelligence (AI) algorithms.

The dataset utilizes 1,083 chest x-ray images from the MIMIC-CXR database and adds transcribed radiology report text, radiologist's dictation audio, and eye-gaze coordinates, according to the researchers.

"All these multimodal data types were collected while a radiologist read the x-ray image, with eye gaze tracked, and the dictation of the report recorded and synced with the eye gaze coordinates," wrote Mehdi Moradi of IBM Research in a blog post on Springer Nature. The researchers shared all the details in an article published online March 25 in Scientific Data.

Of the 1,083 patients, 349 had pneumonia, 330 had congestive heart failure, and 359 were normal. The dataset can help researchers to build domain-expert-guided interpretable AI models, according to Moradi.

"As we demonstrated in the article, we were able to produce more accurate activation maps using this data," Moradi wrote. "In addition, eye-track data and the localization of the disease could be used to devise novel loss functions for training neural networks and perhaps, a deeper synergy between radiologists and AI."

Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.

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(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

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(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

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