Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint

Multiple instance learning (MIL) is a robust paradigm for whole-slide pathological image (WSI) analysis, processing gigapixel-resolution images with slide-level labels. As pioneering efforts, attention-based MIL (ABMIL) and its variants are increasingly becoming popular due to the characteristics of simultaneously handling clinical diagnosis and tumor localization. However, the attention mechanism exhibits limitations in discriminating between instances, which often misclassifies tissues and potentially impairs MIL performance. This paper proposes an Attribute-Driven MIL (AttriMIL) framework to address these issues. Concretely, we dissect the calculation process of ABMIL and present an attribute scoring mechanism that measures the contribution of each instance to bag prediction effectively, quantifying instance attributes. Based on attribute quantification, we develop a spatial attribute constraint and an attribute ranking constraint to model instance correlations within and across slides, respectively. These constraints encourage the network to capture the spatial correlation and semantic similarity of instances, improving the ability of AttriMIL to distinguish tissue types and identify challenging instances. Additionally, AttriMIL employs a histopathology adaptive backbone that maximizes the pre-trained model's feature extraction capability for collecting pathological features. Extensive experiments on three public benchmarks demonstrate that our AttriMIL outperforms existing state-of-the-art frameworks across multiple evaluation metrics. The implementation code is available at https://github.com/MedCAI/AttriMIL.Comment: 10 pages, 8 figure

Dietary Stress From Plant Secondary Metabolites Contributes to Grasshopper (Oedaleus asiaticus) Migration or Plague by Regulating Insect Insulin-Like Signaling Pathway

Diets essentially affect the ecological distribution of insects, and may contribute to or even accelerate pest plague outbreaks. The grasshopper, Oedaleus asiaticus B-Bienko (OA), is a persistent pest occurring in northern Asian grasslands. Migration and plague of this grasshopper is tightly related to two specific food plants, Stipa krylovii Roshev and Leymus chinensis (Trin.) Tzvel. However, how these diets regulate and contribute to plague is not clearly understood. Ecological studies have shown that L. chinensis is detrimental to OA growth due to the presence of high secondary metabolites, and that S. krylovii is beneficial because of the low levels of secondary metabolites. Moreover, in field habitats consisting mainly of these two grasses, OA density has negative correlation to high secondary metabolites and a positive correlation to nutrition content for high energy demand. These two grasses act as a ‘push-pull,’ thus enabling the grasshopper plague. Molecular analysis showed that gene expression and protein phosphorylation level of the IGF → FOXO cascade in the insulin-like signaling pathway (ILP) of OA negatively correlated to dietary secondary metabolites. High secondary metabolites in L. chinensis down-regulates the ILP pathway that generally is detrimental to insect survival and growth, and benefits insect detoxification with high energy cost. The changed ILP could explain the poor growth of grasshoppers and fewer distributions in the presence of L. chinensis. Plants can substantially affect grasshopper gene expression, protein function, growth, and ecological distribution. Down-regulation of grasshopper ILP due to diet stress caused by high secondary metabolites containing plants, such as L. chinensis, results in poor grasshopper growth and consequently drives grasshopper migration to preferable diet, such as S. krylovii, thus contributing to grasshopper plague outbreaks

Orbital-Dependent Electron Correlation in Double-Layer Nickelate La3Ni2O7

The latest discovery of high temperature superconductivity near 80K in La3Ni2O7 under high pressure has attracted much attention. Many proposals are put forth to understand the origin of superconductivity. The determination of electronic structures is a prerequisite to establish theories to understand superconductivity in nickelates but is still lacking. Here we report our direct measurement of the electronic structures of La3Ni2O7 by high-resolution angle-resolved photoemmission spectroscopy. The Fermi surface and band structures of La3Ni2O7 are observed and compared with the band structure calculations. A flat band is formed from the Ni-3dz2 orbitals around the zone corner which is 50meV below the Fermi level. Strong electron correlations are revealed which are orbital- and momentum-dependent. Our observations will provide key information to understand the origin of high temperature superconductivity in La3Ni2O7.Comment: 18 pages, 4 figure