Cell Nuclei Detection and Classification in Whole Slide Images with Transformers

Accurate and efficient cell nuclei detection and classification in histopathological Whole Slide Images (WSIs) are pivotal for digital pathology applications. Traditional cell segmentation approaches, while commonly used, are computationally expensive and require extensive post-processing, limiting their practicality for high-throughput clinical settings. In this paper, we propose a paradigm shift from segmentation to detection for extracting cell information from WSIs, introducing CellNuc-DETR as a more effective solution. We evaluate the accuracy performance of CellNuc-DETR on the PanNuke dataset and conduct cross-dataset evaluations on CoNSeP and MoNuSeg to assess robustness and generalization capabilities. Our results demonstrate state-of-the-art performance in both cell nuclei detection and classification tasks. Additionally, we assess the efficiency of CellNuc-DETR on large WSIs, showing that it not only outperforms current methods in accuracy but also significantly reduces inference times. Specifically, CellNuc-DETR is twice as fast as the fastest segmentation-based method, HoVer-NeXt, while achieving substantially higher accuracy. Moreover, it surpasses CellViT in accuracy and is approximately ten times more efficient in inference speed on WSIs. These results establish CellNuc-DETR as a superior approach for cell analysis in digital pathology, combining high accuracy with computational efficiency.

Layer-wise training for self-supervised learning on graphs

End-to-end training of graph neural networks (GNN) on large graphs presents several memory and computational challenges, and limits the application to shallow architectures as depth exponentially increases the memory and space complexities. In this manuscript, we propose Layer-wise Regularized Graph Infomax, an algorithm to train GNNs layer by layer in a self-supervised manner. We decouple the feature propagation and feature transformation carried out by GNNs to learn node representations in order to derive a loss function based on the prediction of future inputs. We evaluate the algorithm in inductive large graphs and show similar performance to other end to end methods and a substantially increased efficiency, which enables the training of more sophisticated models in one single device. We also show that our algorithm avoids the oversmoothing of the representations, another common challenge of deep GNNs.

RGI : Regularized Graph Infomax for self-supervised learning on graphs

Self-supervised learning is gaining considerable attention as a solution to avoid the requirement of extensive annotations in representation learning on graphs. We introduce \textit{Regularized Graph Infomax (RGI)}, a simple yet effective framework for node level self-supervised learning on graphs that trains a graph neural network encoder by maximizing the mutual information between node level local and global views, in contrast to previous works that employ graph level global views. The method promotes the predictability between views while regularizing the covariance matrices of the representations. Therefore, RGI is non-contrastive, does not depend on complex asymmetric architectures nor training tricks, is augmentation-free and does not rely on a two branch architecture. We run RGI on both transductive and inductive settings with popular graph benchmarks and show that it can achieve state-of-the-art performance regardless of its simplicity.