突破标注瓶颈：人工智能在生物学和医学中的发现
Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine
AI总结：
本文提出通过无监督学习突破生物医学领域标注瓶颈，实现对心脏特征、基因表达和病理检测的高效发现与分析。
摘要：
在将人工智能应用于生物医学领域时，依赖专家标注长期以来一直是主要的限制因素。尽管监督学习推动了临床算法的最初浪潮，但目前正朝着无监督和自监督学习（SSL）的范式转变，正在释放生物银行级数据集的潜在能力。通过直接从数据的内在结构中学习——无论是磁共振成像（MRI）中的像素、体积扫描中的体素，还是基因序列中的标记——这些方法能够发现新的表型，将形态学与遗传学联系起来，并在不引入人类偏见的情况下检测异常。本文综合了“无标签学习”中的经典和最新进展，强调了无监督框架如何推导可遗传的心脏特征、预测组织学中的空间基因表达，并以性能媲美或超越监督方法的方式检测病理情况。
英文摘要：
The dependence on expert annotation has long constituted the primary rate-limiting step in the application of artificial intelligence to biomedicine. While supervised learning drove the initial wave of clinical algorithms, a paradigm shift towards unsupervised and self-supervised learning (SSL) is currently unlocking the latent potential of biobank-scale datasets. By learning directly from the intrinsic structure of data - whether pixels in a magnetic resonance image (MRI), voxels in a volumetric scan, or tokens in a genomic sequence - these methods facilitate the discovery of novel phenotypes, the linkage of morphology to genetics, and the detection of anomalies without human bias. This article synthesises seminal and recent advances in "learning without labels," highlighting how unsupervised frameworks can derive heritable cardiac traits, predict spatial gene expression in histology, and detect pathologies with performance that rivals or exceeds supervised counterparts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
