DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets

Data imbalance is a well-known issue in the field of machine learning, attributable to the cost of data collection, the difficulty of labeling, and the geographical distribution of the data. In computer vision, bias in data distribution caused by image appearance remains highly unexplored. Compared to categorical distributions using class labels, image appearance reveals complex relationships between objects beyond what class labels provide. Clustering deep perceptual features extracted from raw pixels gives a richer representation of the data. This paper presents a novel method for addressing data imbalance in machine learning. The method computes sample likelihoods based on image appearance using deep perceptual embeddings and clustering. It then uses these likelihoods to weigh samples differently during training with a proposed $\textbf{Generalized Focal Loss}$ function. This loss can be easily integrated with deep learning algorithms. Experiments validate the method's effectiveness across autonomous driving vision datasets including KITTI and nuScenes. The loss function improves state-of-the-art 3D object detection methods, achieving over $200\%$ AP gains on under-represented classes (Cyclist) in the KITTI dataset. The results demonstrate the method is generalizable, complements existing techniques, and is particularly beneficial for smaller datasets and rare classes. Code is available at: https://github.com/towardsautonomy/DatasetEquityComment: ICCV 2023 Worksho

Population dynamics of Brachionus calyciflorus driven by the associated natural bacterioplankton

Zooplankton provides bacteria with a complex microhabitat richen in organic and inorganic nutrients, and the bacteria community also changes the physiochemical conditions for zooplankton, where the symbiotic relationship between them plays an important role in the nutrient cycle. However, there are few studies on the effect of associated bacteria on the population dynamics of rotifers. In order to make clear their relationships, we reconstructed the associated bacterial community in Brachionus calyciflorus culture, and examined the life history and population growth parameters, and analyzed the diversity and community composition of the associated bacteria at different growth stages of B. calyciflorus. The results showed that the addition of bacteria from natural water can promote the population growth and asexual reproduction of B. calyciflorus, but has no significant effect on sexual reproduction, exhibited by the improvement of its life expectancy at hatching, net reproduction rates and intrinsic growth rate, no significant effects on the generation time and mixis ratio of offspring. It was found that the B. calyciflorus-associated bacterial community was mainly composed of Proteobacteria, Bacteroidota, Actinobacteriota, Cyanobacteria and Firmicutes. Through correlation network analysis, the members of Burkholderiales, Pseudomonadales, Micrococcales, Caulobacterales and Bifidobacteriales were the keystone taxa of B. calyciflorus-associated bacteria. In addition, the relative abundance of some specific bacteria strains increased as the population density of B. calyciflorus increased, such as Hydrogenophaga, Acidovorax, Flavobacterium, Rheinheimera, Novosphingobium and Limnobacter, and their relative abundance increased obviously during the slow and exponential phases of population growth. Meanwhile, the relative abundance of adverse taxa (such as Elizabethkingia and Rickettsiales) decreased significantly with the increase in rotifer population density. In conclusion, the closely associated bacteria are not sufficient for the best growth of B. calyciflorus, and external bacterioplankton is necessary. Furthermore, the function of keystone and rare taxa is necessary for further exploration. The investigation of the symbiotic relationship between zooplankton-associated bacterial and bacterioplankton communities will contribute to monitoring their roles in freshwater ecosystems, and regulate the population dynamics of the micro-food web

Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classificatio

Secreted Frizzled Related Protein 2 Modulates Epithelial–Mesenchymal Transition and Stemness via Wnt/β-Catenin Signaling in Choriocarcinoma

Background/Aims: Choriocarcinoma (CC) is a highly aggressive gestational trophoblastic neoplasia; however, the underlying molecular mechanisms of its invasiveness and metastasis remain poorly understood. Human secreted frizzled-related protein 2 (SFRP2) could function as a tumor promoter or suppressor in different tumors, yet the role it plays in CC’s invasion and metastasis is thoroughly unclear. The current study was aimed to explore the function and underlying mechanism of SFRP2 in CC. Methods: The expression of SFRP2 in CC tissues was examined via immunohistochemistry. The methylation level and expression of SFRP2 in CC cell lines, JEG-3 and JAR were examined via bisulfite sequencing PCR (BSP), western blotting and quantitative RT-PCR. The biological role of increasing expressed SFRP2 through its promoter demethylation with 5-Aza-2’-deoxycytidine (5-Aza) was examined by a series of in vitro functional studies. Furthermore, lentivirus transfection technology was adopted to investigate the biological roles of SFRP2 knockdown in JEG-3 and JAR cells in vitro and in vivo. Moreover, its downstream signaling pathway was investigated. Results: SFRP2 was downregulated in CC tissues, and its expression was inversely related to its promoter hypermethylation frequency in JEG-3 and JAR cells. Increased SFRP2 through its promoter demethylation inhibited cell migration, invasion and colony formation in JEG-3 and JAR cells, whereas decreased SFRP2 reversed the epithelial-mesenchymal transition (EMT) process and stemness in JEG-3 and JAR cells both in vitro and vivo. Mechanistically, SFRP2 regulated the EMT and stemness of CC cell lines via canonical Wnt/β-catenin signaling, validated by the usage of a Wnt activator and inhibitor. Conclusion: The current study indicates that downregulated SFRP2 has potent tumor-promotive effects in CC through the modulation of cancer stemness and the EMT phenotype via activation of Wnt/β-catenin signaling in vitro and in vivo