Xiaohuan Cao – författare

Tongxuan's 100 Chan Questions is a significant Zen Buddhist koan collection. It includes dialogues about philosophy and religious practice expressed through paradoxical sayings and puzzling poems. This thirteenth-century text, included in the supplementary Buddhist canon, was composed by several leading figures of the Caodong Chan Buddhist school in China, who were part of an important sublineage located in Beijing and supported by the emerging Mongol rulers. The collection thus represents the literary production of a crucial, though lesser-known, phase of Caodong Chan history based in a branch situated in the northern territories that thrived at the dawn of the Yuan dynasty.The text of Tongxuan's 100 Chan Questions demonstrates key aspects of Caodong discursive practice, reflecting the school's philosophy that Chan transmission should be based on a rhetoric of minimalism, or an exceptionally concise and cryptic writing style that conjures how “to speak without speaking.” This technique is particularly suited to cultivating a manner of contemplative training based on the notion of pragmatism, or the application of a nondual standpoint to examples of everyday moral decision-making.This elegant translation of Tongxuan's 100 Chan Questions with insightful commentary on the meaning and symbolism of the cases provided by the author shows how and why the collection was compiled by several generations of Chan monks who gained the support of the Mongol government. Steven Heine examines the diverse ways in which the text's Zen sayings and poems were influenced by a wide variety of sources encompassing Buddhist sutras, Daoist parables, Confucian dictums, Chinese literary compositions, and traditional East Asian folklore and mythology.

This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.*The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually.

This book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022.The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

The two-volume set LNCS 14348 and 14139 constitutes the proceedings of the 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023, held in conjunction with MICCAI 2023, in Vancouver, Canada, in October 2023. The 93 full papers presented in the proceedings were carefully reviewed and selected from 139 submissions. They focus on major trends and challenges in artificial intelligence and machine learning in the medical imaging field, translating medical imaging research into clinical practice. Topics of interests included deep learning, generative adversarial learning, ensemble learning, transfer learning, multi-task learning, manifold learning, reinforcement learning, along with their applications to medical image analysis, computer-aided diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.