  Accurate segmentation of lesions is crucial for diagnosis and treatment of
early esophageal cancer (EEC). However, neither traditional nor deep
learning-based methods up to today can meet the clinical requirements, with the
mean Dice score - the most important metric in medical image analysis - hardly
exceeding 0.75. In this paper, we present a novel deep learning approach for
segmenting EEC lesions. Our approach stands out for its uniqueness, as it
relies solely on a single image coming from one patient, forming the so-called
"You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network"
learning ensures complete patient privacy as it does not use any images from
other patients as the training data. On the other hand, it avoids nearly all
generalization-related problems since each trained network is applied only to
the input image itself. In particular, we can push the training to
"over-fitting" as much as possible to increase the segmentation accuracy. Our
technical details include an interaction with clinical physicians to utilize
their expertise, a geometry-based rendering of a single lesion image to
generate the training set (the \emph{biggest} novelty), and an edge-enhanced
UNet. We have evaluated YOHO over an EEC data-set created by ourselves and
achieved a mean Dice score of 0.888, which represents a significant advance
toward clinical applications.
