L’esprit du matérialisme et son destin

Bloch, Olivier

doi:10.3917/lp.382.0047

Cited by 1 publication

(2 citation statements)

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“…Heart realted datasets and challenges: Most of these datasets and challenges (112,119,125,130,132,134,140) are related to the heart segmentation. The most frequently used modalities are MR and ultrasound, and the aim is to segment the heart into the left atrium, chambers, valves, and other parts.…”

Section: Chest and Abdomen Organ Parts Segmentationmentioning

confidence: 99%

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A Systematic Collection of Medical Image Datasets for Deep Learning

Li¹,

Zhu²,

Cong³

et al. 2021

Preprint

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The astounding success made by artificial intelligence (AI) in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. The lack of data in the medical imaging field creates a bottleneck for the application of deep learning to medical image analysis. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require many resources, such as human expertise and funding. That makes it difficult for non-medical researchers to have access to useful and large medical data. Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected information of around three hundred datasets and challenges mainly reported between 2013 and 2020 and categorized them into four categories: head & neck, chest & abdomen, pathology & blood,

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Section: Chest and Abdomen Organ Parts Segmentationmentioning

confidence: 99%

“…-Lung cancer and tumor (173,180,189,195,197) -Pulmonary nodule (162,174,193,197,200) -Celiac-related damage (202,203,204,206) -Other lung lesions (172,183) -Polyp (198,204) -Cervical cancer ( 182 Furthermore, challenges (203,206) focus on the segmentation of artifacts (e. g., polyps) in endoscopic images.…”

Section: Chest and Abdomen Datasets For Detectionmentioning

confidence: 99%