Late Cretaceous ammonoids show that drivers of diversification are regionally heterogeneous

Palaeontologists have long sought to explain the diversification of individual clades to whole biotas at global scales. Advances in our understanding of the spatial distribution of the fossil record through geological time, however, has demonstrated that global trends in biodiversity were a mosaic of regionally heterogeneous diversification processes. Drivers of diversification must presumably have also displayed regional variation to produce the spatial disparities observed in past taxonomic richness. Here, we analyse the fossil record of ammonoids, pelagic shelled cephalopods, through the Late Cretaceous, characterised by some palaeontologists as an interval of biotic decline prior to their total extinction at the Cretaceous-Paleogene boundary. We regionally subdivide this record to eliminate the impacts of spatial sampling biases and infer regional origination and extinction rates corrected for temporal sampling biases using Bayesian methods. We then model these rates using biotic and abiotic drivers commonly inferred to influence diversification. Ammonoid diversification dynamics and responses to this common set of diversity drivers were regionally heterogeneous, do not support ecological decline, and demonstrate that their global diversification signal is influenced by spatial disparities in sampling effort. These results call into question the feasibility of seeking drivers of diversity at global scales in the fossil record

Late Cretaceous ammonoids show that drivers of diversification are regionally heterogeneous

Palaeontologists have long sought to explain the diversification of individual clades to whole biotas at global scales. Advances in our understanding of the spatial distribution of the fossil record through geological time, however, has demonstrated that global trends in biodiversity were a mosaic of regionally heterogeneous diversification processes. Drivers of diversification must presumably have also displayed regional variation to produce the spatial disparities observed in past taxonomic richness. Here, we analyse the fossil record of ammonoids, pelagic shelled cephalopods, through the Late Cretaceous, characterised by some palaeontologists as an interval of biotic decline prior to their total extinction at the Cretaceous-Paleogene boundary. We regionally subdivide this record to eliminate the impacts of spatial sampling biases and infer regional origination and extinction rates corrected for temporal sampling biases using Bayesian methods. We then model these rates using biotic and abiotic drivers commonly inferred to influence diversification. Ammonoid diversification dynamics and responses to this common set of diversity drivers were regionally heterogeneous, do not support ecological decline, and demonstrate that their global diversification signal is influenced by spatial disparities in sampling effort. These results call into question the feasibility of seeking drivers of diversity at global scales in the fossil record

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed ``unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956)

Cross-scale Multi-instance Learning for Pathological Image Diagnosis

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL

Nucleus subtype classification using inter-modality learning

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology