Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020

Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020

Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training ( = 199, source A), validation ( = 50, source A) and testing ( = 23, source A; = 23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge — 2020

Measurement of branching fractions of Λc+→pKS0KS0\Lambda_c^+\to{}pK_S^0K_S^0 and Λc+→pKS0η\Lambda_c^+\to{}pK_S^0\eta at Belle

We present a study of a singly Cabibbo-suppressed decay $\Lambda_c^+\to{}pK_S^0K_S^0$ and a Cabibbo-favored decay $\Lambda_c^+\to{}pK_S^0\eta$ based on 980 $\rm fb^{-1}$ of data collected by the Belle detector, operating at the KEKB energy-asymmetric $e^+e^-$ collider. We measure their branching fractions relative to $\Lambda_c^+\to{}pK_S^0$: $\mathcal{B}(\Lambda_c^+\to{}pK_S^0K_S^0)/\mathcal{B}(\Lambda_c^+\to{}pK_S^0)={(1.48 \pm 0.08 \pm 0.04)\times 10^{-2}}$ and $\mathcal{B}(\Lambda_c^+\to{}pK_S^0\eta)/\mathcal{B}(\Lambda_c^+\to{}pK_S^0)={(2.73\pm 0.06\pm 0.13)\times 10^{-1}}$. Combining with the world average $\mathcal{B}(\Lambda_c^+\to{}pK_S^0)$, we have the absolute branching fractions: $\mathcal{B}(\Lambda_c^+\to{}pK_S^0K_S^0) = {(2.35\pm 0.12\pm 0.07 \pm 0.12 )\times 10^{-4}}$ and $\mathcal{B}(\Lambda_c^+\to{}pK_S^0\eta) = {(4.35\pm 0.10\pm 0.20 \pm 0.22 )\times 10^{-3}}$. The first and second uncertainties are statistical and systematic, respectively, while the third ones arise from the uncertainty on $\mathcal{B}(\Lambda_c^+\to{}pK_S^0)$. The mode $\Lambda_c^+\to{}pK_S^0K_S^0$ is observed for the first time and has a statistical significance of $>\!10\sigma$. The branching fraction of $\Lambda_c^+\to{}pK_S^0\eta$ has been measured with a threefold improvement in precision over previous results and is found to be consistent with the world average