更强并非更好：对比学习中用于医学图像分割的改进增强

自监督对比学习的特征增强：更深入的探讨

Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look

本文提出一种与领域无关的特征增强统一框架，通过在特征空间增强数据多样性，系统研究其架构与原则，提升了自监督对比学习预训练模型的泛化能力与鲁棒性。

Saliency Can Be All You Need In Contrastive Self-Supervised Learning

We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection. This detection technique, which had been devised for unrelated Computer Vision tasks, was empirically observed to play the role of an augmentation facilitator within the SSL protocol. This observation is rooted in our practical attempts to learn, by SSL-fashion, aerial imagery of solar panels, which exhibit challenging boundary patterns. Upon the successful integration of this techniq…

PCLMix：基于像素级对比学习与动态混合增强的弱监督医学图像分割

PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation

针对弱监督医学图像分割中监督信号传播易过度扩散的问题，提出含动态混合增强、像素级对比学习与双一致性正则化的PCLMix框架，在ACDC数据集上缩小了与全监督方法的性能差距。

Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases

Self-supervised representation learning approaches have recently surpassed their supervised learning counterparts on downstream tasks like object detection and image classification. Somewhat mysteriously the recent gains in performance come from training instance classification models, treating each image and it's augmented versions as samples of a single class. In this work, we first present quantitative experiments to demystify these gains. We demonstrate that approaches like MOCO and PIRL learn occlusion-invariant representations. However, they fail to capture viewpoint and category instanc…

SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata

Computer-aided diagnosis plays a salient role in more accessible and accurate cardiopulmonary diseases classification and localization on chest radiography. Millions of people get affected and die due to these diseases without an accurate and timely diagnosis. Recently proposed contrastive learning heavily relies on data augmentation, especially positive data augmentation. However, generating clinically-accurate data augmentations for medical images is extremely difficult because the common data augmentation methods in computer vision, such as sharp, blur, and crop operations, can severely alt…