Results 111 to 120 of about 136,861 (303)

Cell Segmentation Without Annotation by Unsupervised Domain Adaptation Based on Cooperative Self-Learning

IEEE Access
Semantic cell segmentation from microscopic images is essential for the quantitative evaluation of cell morphology. Although supervised deep-learning-based models offer accurate segmentation, their performance degrades for unknown cell types.
Shintaro Miyaki, Takashi Morikura, Shori Nishimoto, Yuta Tokuoka, Takahiro G. Yamada, Akira Funahashi   +5 more
doaj   +1 more source

Learning by Unsupervised Nonlinear Diffusion

CoRR, 2018
40 Pages, 17 ...
Mauro Maggioni, James M. Murphy
openaire   +4 more sources

Transferable Deep Reinforcement Learning With Edge‐Contour‐Depth Fusion for Autonomous Wireless Capsule Endoscopy Navigation

Advanced Science, EarlyView.
This study presents an anatomical landmark‐guided DRL framework for autonomous wireless capsule endoscopy navigation. Using a lightweight edge‐contour‐depth fusion module, it achieves over 97% coverage across diverse gastric anatomies. To ensure reliability, a two‐stage sim‐to‐real pipeline with an adaptive dynamic programming controller mitigates ...
Haoxuan Wu, Haitao Gao, Qingyang Liu, Sishen Yuan, Haiyang Fang, Mingwu Su, Baijia Liang, Yongzun Yang, Long Bai, Wenzhen Dong, Dihong Xie, Shijian Su, Jiewen Lai, Shing Shin Cheng, Zhen Li, Xiuli Zuo, Hongliang Ren   +16 more
wiley   +1 more source

Improving morphology induction by learning spelling rules

, 2009
Unsupervised learning of morphology is an important task for human learners and in natural language processing systems. Previous systems focus on segmenting words into substrings (taking ⇒ tak.ing), but sometimes a segmentation-only analysis is ...
Naradowski, Jason, Goldwater, Sharon; id_orcid   +1 more
core  

Allosteric DNAzyme‐Enabled Sensitive and Multiplex Detection of Biomarkers for Rapid Diagnosis of Urinary Tract Infections

Advanced Science, EarlyView.
A catalytic allosteric DNAzyme assay (SMART) is developed by engineering DNAzyme into a unimolecular biosensor, enabling highly sensitive and multiplex detection of small molecules and nucleic acids. SMART enables extraction‐free, one‐pot, one‐step, isothermal, and cost‐effective detection of biological markers, achieving >95% accuracy for rapid ...
Yanzhe Shen, Chenzhi Shi, Xiaowei Ma, Pengfei Hou, Haomin Zhang, Juanxiu Qin, Li Pan, Guilin Li, Lifei Gao, Qian Ma, Donglei Yang, Min Li, Pengfei Wang   +12 more
wiley   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Pattern classification via unsupervised learners [PDF]

We consider classification problems in a variant of the Probably Approximately Correct (PAC)-learning framework, in which an unsupervised learner creates a discriminant function over each class and observations are labeled by the learner returning the ...
Palmer, Nicholas James
core  

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

From Label‐Free Multiphoton Imaging to Pathological Reports: A Vision‐Language Breast Cancer Margin Pathological Diagnosis System

Advanced Science, EarlyView.
MarginPath is a novel vision‐language system that automates breast cancer margin assessment using a single label‐free multiphoton microscopy image. By integrating tumor‐associated collagen signatures with virtual H&E imaging, it generates accurate margin heatmaps and comprehensive diagnostic reports.
Shu Wang, Jingze Su, Xiahui Han, Deyong Kang, Xiao Zhang, Fei Xu, Changzu Liu, Junlin Pan, Xingfu Wang, Qiaohui Zhan, Aimin Wang, Feng Huang, Heping Cheng, Wenxi Liu, Ruolan Lin, Jianxin Chen   +15 more
wiley   +1 more source

Unsupervised Segmentation of Object Manipulation Operations from Multimodal Input

, 2011
Barchunova A, Moringen J, Großekathöfer U, et al. Unsupervised Segmentation of Object Manipulation Operations from Multimodal Input. In: Hammer B, Villmann T, eds. Machine Learning Reports. New Challenges in Neural Computation.
Großekathöfer, Ulf, Barchunova, Alexandra ; https://orcid.org/, Moringen, Jan, Janssen, Herbert, Ritter, Helge ; https://orcid.org/, Haschke, Robert ; https://orcid.org/, Villmann, Thomas, Wachsmuth, Sven ; https://orcid.org/, Hammer, Barbara   +8 more
core