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Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge ... and pancreatic cancer. The internal validation set consisted of 200 images: 100 with the same disease type as the development set and 100 with new abdominal diseases, such as stomach cancer, sarcomas, colon ...
The challenge aims to benchmark algorithms that can automatically segment 13 abdominal organs from CT scans, including the liver, right kidney, spleen, pancreas, aorta, inferior vena cava, right adrenal gland, left adrenal gland, gallbladder, esophagus, stomach, duodenum, and left kidney (Fig. 1a).
10 Αυγ 2023 · View a PDF of the paper titled Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge, by Jun Ma and 28 other authors. Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning.
5 Απρ 2022 · This study aimed to quantify the risk of cancers in the abdominal cavity or cancers that may present with abdominal pain (termed ‘intra-abdominal’) in patients aged ≥40 years in the year after newly reported abdominal pain, with or without other features of intraabdominal cancer.
10 Αυγ 2023 · covering North American and European patients with typical abdominal diseases: liver cancer, kidney cancer, spleen disease, and pancreas cancer . The internal validation set consists of 200...
- "Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge" TABLE 1 Dataset characteristics of the development set, internal validation set, and three external validation sets.
10 Μαρ 2020 · The data set contains 25 anatomic sites with 32 cancer subtypes. Ten tumor types (brain, endocrine, gastrointestinal tract, gynecological, hematopoietic, liver/pancreaticobiliary, melanocytic ...
