The lesion segmentation on endoscopic images is challenging due to its
complex and ambiguous features. Fully-supervised deep learning segmentation
methods can receive good performance based on entirely pixel-level labeled
dataset but greatly increase experts' labeling burden. Semi-supervised and
weakly supervised methods can ease labeling burden, but heavily strengthen the
learning difficulty. To alleviate this difficulty, weakly semi-supervised
segmentation adopts a new annotation protocol of adding a large number of point
annotation samples into a few pixel-level annotation samples. However, existing
methods only mine points' limited information while ignoring reliable prior
surrounding the point annotations. In this paper, we propose a weakly
semi-supervised method called Point-Neighborhood Learning (PNL) framework. To
mine the prior of the pixels surrounding the annotated point, we transform a
single-point annotation into a circular area named a point-neighborhood. We
propose point-neighborhood supervision loss and pseudo-label scoring mechanism
to enhance training supervision. Point-neighborhoods are also used to augment
the data diversity. Our method greatly improves performance without changing
the structure of segmentation network. Comprehensive experiments show the
superiority of our method over the other existing methods, demonstrating its
effectiveness in point-annotated medical images. The project code will be
available on: this https URL

通过引入点邻域学习、增强训练监督及扩充数据多样性，我们提出了一种弱半监督方法，名为 Point-Neighborhood Learning (PNL) 框架，用于医学图像中的点注释，显著提高了性能并不改变分割网络结构。

鼻内镜图像分割的点邻域学习框架

A Point-Neighborhood Learning Framework for Nasal Endoscope Image  Segmentation

This paper presents a new practical training method for human matting, which
demands delicate pixel-level human region identification and significantly
laborious annotations. To reduce the annotation cost, most existing matting
approaches often rely on image synthesis to augment the dataset. However, the
unnaturalness of synthesized training images brings in a new domain
generalization challenge for natural images. To address this challenge, we
introduce a new learning paradigm, weakly semi-supervised human matting
(WSSHM), which leverages a small amount of expensive matte labels and a large
amount of budget-friendly segmentation labels, to save the annotation cost and
resolve the domain generalization problem. To achieve the goal of WSSHM, we
propose a simple and effective training method, named Matte Label Blending
(MLB), that selectively guides only the beneficial knowledge of the
segmentation and matte data to the matting model. Extensive experiments with
our detailed analysis demonstrate our method can substantially improve the
robustness of the matting model using a few matte data and numerous
segmentation data. Our training method is also easily applicable to real-time
models, achieving competitive accuracy with breakneck inference speed (328 FPS
on NVIDIA V100 GPU). The implementation code is available at
https://github.com/clovaai/WSSHM.

本研究提出了一种新的实用人像抠图训练方法，通过精细的像素级人体区域识别和大量繁重的标注来实现。为了降低标注成本，现有的抠图方法通常依赖于图像合成来增加数据集。然而，合成训练图像的不自然性给自然图像带来了新的领域泛化挑战。为了解决这个挑战，我们引入了一种新的学习范式，弱半监督人像抠图（WSSHM），它利用少量昂贵的抠图标签和大量经济实惠的分割标签来节省标注成本和解决领域泛化问题。为了实现 WSSHM 的目标，我们提出了一种简单有效的训练方法，称为抠图标签融合（MLB），仅选择性地将分割和抠图数据的有益知识引导到抠图模型。通过详细分析的大量实验表明，我们的方法能够在使用少量抠图数据和大量分割数据的情况下显著提高抠图模型的鲁棒性。我们的训练方法也适用于实时模型，并具有竞争力的准确性和极快的推理速度（在 NVIDIA V100 GPU 上为 328 FPS）。实现代码可在 https://github.com/clovaai/WSSHM 获得。

向标签有效的人物抠图迈进：无需辅助轮廓线的弱监督简易基线

Towards Label-Efficient Human Matting: A Simple Baseline for Weakly  Semi-Supervised Trimap-Free Human Matting

In this paper, we introduce a novel learning scheme named weakly
semi-supervised instance segmentation (WSSIS) with point labels for
budget-efficient and high-performance instance segmentation. Namely, we
consider a dataset setting consisting of a few fully-labeled images and a lot
of point-labeled images. Motivated by the main challenge of semi-supervised
approaches mainly derives from the trade-off between false-negative and
false-positive instance proposals, we propose a method for WSSIS that can
effectively leverage the budget-friendly point labels as a powerful weak
supervision source to resolve the challenge. Furthermore, to deal with the hard
case where the amount of fully-labeled data is extremely limited, we propose a
MaskRefineNet that refines noise in rough masks. We conduct extensive
experiments on COCO and BDD100K datasets, and the proposed method achieves
promising results comparable to those of the fully-supervised model, even with
50% of the fully labeled COCO data (38.8% vs. 39.7%). Moreover, when using as
little as 5% of fully labeled COCO data, our method shows significantly
superior performance over the state-of-the-art semi-supervised learning method
(33.7% vs. 24.9%). The code is available at
this https URL

本文提出了一种名为 WSSIS 的学习方法，使用点标签实现高效的实例分割，同时引入 MaskRefineNet 来处理少量标签数据的情况，并在 COCO 数据集上进行了丰富的实验，获得了比当前半监督方法更好的表现 (33.7% vs. 24.9%)，代码提供在 https URL 上。