We present CSSL, a contrastive semi-supervised method for medical image segmentation. CSSL samples class-wise patches from labelled images and from confident pseudo-labels, separates their embeddings with a supervised contrastive loss with mixed hard negatives, and uses the Silhouette coefficient of the embedding clusters to set the strength of a feature-perturbation consistency loss. On MoNuSeg, patch embeddings are obtained by masked pooling of the encoder features of the unmasked image, nucleus rims form an additional contrastive class and a memory bank stores past embeddings; on CHAOS, patches are pixel-masked and no memory bank is used. The CSSL losses are added to a weak-to-strong pseudo-labelling recipe. We follow the protocols of published baselines: Self-Loop on MoNuSeg and DAFNet on CHAOS. On MoNuSeg with 50% labels, CSSL reaches 80.87 $\pm$ 0.17 nuclei F1 over three label splits, compared with 79.10 for Self-Loop and 78.73 for the same recipe without CSSL. On CHAOS T2 with 25% labels, CSSL reaches 89.01 Dice, compared with 82.0 for DAFNet and 88.94 for the recipe alone. Ablations show that the contrastive branch and, on MoNuSeg, the boundary class and feature-level pooling account for most of the improvement. Code: https://github.com/prinshul/CSSL
Strong data augmentation is a fundamental component of state-of-the-art mean teacher-based Source-Free domain adaptive Object Detection (SFOD) methods, enabling consistency-based self-supervised optimization along weak augmentation. However, our theoretical analysis and empirical observations reveal a critical limitation: strong augmentation can inadvertently erase class-relevant components, leading to artificial inter-category confusion. To address this issue, we introduce Weak-to-strong Semantics Compensation (WSCo), a novel remedy that leverages weakly augmented images, which preserve full semantics, as anchors to enrich the feature space of their strongly augmented counterparts. Essentially, this compensates for the class-relevant semantics that may be lost during strong augmentation on the fly. Notably, WSCo can be implemented as a generic plug-in, easily integrable with any existing SFOD pipelines. Extensive experiments validate the negative impact of strong augmentation on detection performance, and the effectiveness of WSCo in enhancing the performance of previous detection models on standard benchmarks.
Human exploration of the moon is expected to resume in the next decade, following the last such activities in the Apollo programme time. One of the major objectives of returning to the Moon is to continue retrieving geological samples, with a focus on collecting high-quality specimens to maximize scientific return. Tools that assist astronauts in making informed decisions about sample collection activities can maximize the scientific value of future lunar missions. A lunar rock classifier is a tool that can potentially provide the necessary information for astronauts to analyze lunar rock samples, allowing them to augment in-situ value identification of samples. Towards demonstrating the value of such a tool, in this paper, we introduce a framework for classifying rock types in thin sections of lunar rocks. We leverage the vast collection of petrographic thin-section images from the Apollo missions, captured under plane-polarized light (PPL), cross-polarised light (XPL), and reflected light at varying magnifications. Advanced machine learning methods, including contrastive learning, are applied to analyze these images and extract meaningful features. The contrastive learning approach fine-tunes a pre-trained Inception-Resnet-v2 network with the SimCLR loss function. The fine-tuned Inception-Resnet-v2 network can then extract essential features effectively from the thin-section images of Apollo rocks. A simple binary classifier is trained using transfer learning from the fine-tuned Inception-ResNet-v2 to 98.44\% ($\pm$1.47) accuracy in separating breccias from basalts.