Federated learning (FL) methods for multi-organ segmentation in CT scans are gaining popularity, but generally require numerous rounds of parameter exchange between a central server and clients. This repetitive sharing of parameters between server and clients may not be practical due to the varying network infrastructures of clients and the large transmission of data. Further increasing repetitive sharing results from data heterogeneity among clients, i.e., clients may differ with respect to the type of data they share. For example, they might provide label maps of different organs (i.e. partial labels) as segmentations of all organs shown in the CT are not part of their clinical protocol. To this end, we propose an efficient communication approach for FL with partial labels. Specifically, parameters of local models are transmitted once to a central server and the global model is trained via knowledge distillation (KD) of the local models. While one can make use of unlabeled public data as inputs for KD, the model accuracy is often limited due to distribution shifts between local and public datasets. Herein, we propose to generate synthetic images from clients' models as additional inputs to mitigate data shifts between public and local data. In addition, our proposed method offers flexibility for additional finetuning through several rounds of communication using existing FL algorithms, leading to enhanced performance. Extensive evaluation on public datasets in few communication FL scenario reveals that our approach substantially improves over state-of-the-art methods.

Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately such anomalies requires models to reason about various component types through segmentation. However, curation of pixel-level annotations for semantic segmentation is both time-consuming and expensive. Although there are some prior few-shot or unsupervised co-part segmentation algorithms, they often fail on images with industrial object. These images have components with similar textures and shapes, and a precise differentiation proves challenging. In this study, we introduce a novel component segmentation model for LA detection that leverages a few labeled samples and unlabeled images sharing logical constraints. To ensure consistent segmentation across unlabeled images, we employ a histogram matching loss in conjunction with an entropy loss. As segmentation predictions play a crucial role, we propose to enhance both local and global sample validity detection by capturing key aspects from visual semantics via three memory banks: class histograms, component composition embeddings and patch-level representations. For effective LA detection, we propose an adaptive scaling strategy to standardize anomaly scores from different memory banks in inference. Extensive experiments on the public benchmark MVTec LOCO AD reveal our method achieves 98.1% AUROC in LA detection vs. 89.6% from competing methods.