Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves automating cell classification through use of deep learning-based techniques. In practice, a large amount of data is required to accurately train these deep learning models. However, due to the sparse human cell datasets currently available, the performance of these models is typically low. This study investigates the feasibility of using few-shot learning-based techniques to mitigate the data requirements for accurate training. The study is comprised of three parts: First, current state-of-the-art few-shot learning techniques are evaluated on human cell classification. The selected techniques are trained on a non-medical dataset and then tested on two out-of-domain, human cell datasets. The results indicate that, overall, the test accuracy of state-of-the-art techniques decreased by at least 30% when transitioning from a non-medical dataset to a medical dataset. Second, this study evaluates the potential benefits, if any, to varying the backbone architecture and training schemes in current state-of-the-art few-shot learning techniques when used in human cell classification. Even with these variations, the overall test accuracy decreased from 88.66% on non-medical datasets to 44.13% at best on the medical datasets. Third, this study presents future directions for using few-shot learning in human cell classification. In general, few-shot learning in its current state performs poorly on human cell classification. The study proves that attempts to modify existing network architectures are not effective and concludes that future research effort should be focused on improving robustness towards out-of-domain testing using optimization-based or self-supervised few-shot learning techniques.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
City University of Hong Kong（香港城市大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；University of Oxford（牛津大学）；Harvard University（哈佛大学）；Nanyang Technological University（南洋理工大学）
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Prompt2Auto: From Motion Prompt to Automated Control via Geometry-Invariant One-Shot Gaussian Process Learning
Chair of Robotics and Systems Intelligence, Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（机器人与系统智能教授团，慕尼黑机器人与机器智能研究所，慕尼黑技术大学）；Chair of Information-oriented Control, TUM School of Computation, Information and Technology, Technical University of Munich（信息导向控制教授团，技术大学计算、信息与技术学院，慕尼黑技术大学）；Dalian University of Technology（大连理工大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning
Department of Radiology, Northwestern University, Chicago, IL, USA（放射科，西北大学，芝加哥，伊利诺伊州，美国）；Department of Cell and Developmental Biology, Northwestern University, Chicago, IL, USA（细胞与发育生物学系，西北大学，芝加哥，伊利诺伊州，美国）；Department of Pathology, Northwestern University, Chicago, IL, USA（病理学系，西北大学，芝加哥，伊利诺伊州，美国）
Few-shot Neural Human Performance Rendering from Sparse RGBD Videos
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Few-Shot Object Detection with Sparse Context Transformers
School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Zhengzhou University（郑州大学）；National Supercomputing Center in Zhengzhou（郑州国家超级计算中心）；Sorbonne University（索邦大学）
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation
CFCS, School of Computer Science, Peking University（北京大学计算机学院CFCS）；Institute for AI, Peking University（北京大学人工智能研究院）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）；PKU-WUHAN Institute for Artificial Intelligence, China（北京大学武汉人工智能研究院）
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy（人工智能与计算机系统单位，工程系，罗马生物医学大学）
Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography
ETH Zürich, Institute for Particle Physics and Astrophysics（苏黎世联邦理工学院，粒子物理与天体物理研究所）；University of Cambridge, Department of Physics（剑桥大学，物理系）；University of Cincinnati, Department of Physics（辛辛那提大学，物理系）；University of Cambridge, Department of Radiology（剑桥大学，放射学系）
ContextMaster: Interactive Multi-Shot Video Creation via Fixed-Budget Sparse Context Routing
Tsinghua University（清华大学）；Nanjing University（南京大学）；Kling Team, Kuaishou Technology（快手科技影团队）；ShanghaiTech University（上海科技大学）；The Hong Kong University of Science and Technology（香港科技大学）
Zero-shot large vision-language model prompting for automated bone identification in paleoradiology x-ray archives
Vanderbilt University（范德堡大学）；Vanderbilt University School of Medicine（范德堡大学医学院）；Vanderbilt University Medical Center（范德堡大学医学中心）；Museum of London（伦敦博物馆）；Ossafreelance
LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；Mercedes-Benz Research & Development North America（梅赛德斯-奔驰北美研发中心）；University of Stuttgart（斯图加特大学）；University of Southampton（南安普顿大学）
