ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images

Detectingandsegmentingobjectswithinwholeslideimagesis essential in computational pathology workflow. Self-supervised learning (SSL) is appealing to such annotation-heavy tasks. Despite the extensive benchmarks in natural images for dense tasks, such studies are, unfortunately, absent in current works for pathology. Our paper intends to narrow this gap. We first benchmark representative SSL methods for dense prediction tasks in pathology images. Then, we propose concept contrastive learning (ConCL), an SSL framework for dense pre-training. We explore how ConCL performs with concepts provided by different sources and end up with proposing a simple dependency-free concept generating method that does not rely on external segmentation algorithms or saliency detection models. Extensive experiments demonstrate the superiority of ConCL over previous state-of-the-art SSL methods across different settings. Along our exploration, we distll several important and intriguing components contributing to the success of dense pre-training for pathology images. We hope this work could provide useful data points and encourage the community to conduct ConCL pre-training for problems of interest. Code is available.Comment: Accepted as an ECCV 2022 paper. Code is available at https://github.com/Jiawei-Yang/ConCL or https://github.com/TencentAILabHealthcare/ConC

Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation

Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists' heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. Inspired by the recent success of self-supervised learning methods, we present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. Particularly, taking advantage of self-supervised contrastive learning, an encoder is trained to project the unlabeled histopathology image patches into a discriminative embedding space where these patches are clustered to identify the tissue prototypes by efficient pathologists' visual examination. Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary. Finally, the pseudo masks are used to train a segmentation network with dense supervision for better performance. Experiments on two public datasets demonstrate that our human-machine interactive tissue prototype learning method can achieve comparable segmentation performance as the fully-supervised baselines with less annotation burden and outperform other weakly-supervised methods. Codes will be available upon publication.Comment: IPMI2023 camera read

Analysis of surface folding patterns of diccols using the GPU-Optimized geodesic field estimate

Localization of cortical regions of interests (ROIs) in the human brain via analysis of Diffusion Tensor Imaging (DTI) data plays a pivotal role in basic and clinical neuroscience. In recent studies, 358 common cortical landmarks in the human brain, termed as Dense Indi- vidualized and Common Connectivity-based Cortical Landmarks (DICCCOLs), have been identified. Each of these DICCCOL sites has been observed to possess fiber connection patterns that are consistent across individuals and populations and can be regarded as predictive of brain function. However, the regularity and variability of the cortical surface fold patterns at these DICCCOL sites have, thus far, not been investigated. This paper presents a novel approach, based on intrinsic surface geometry, for quantitative analysis of the regularity and variability of the cortical surface folding patterns with respect to the structural neural connectivity of the human brain. In particular, the Geodesic Field Estimate (GFE) is used to infer the relationship between the structural and connectional DTI features and the complex surface geometry of the human brain. A parallel algorithm, well suited for implementation on Graphics Processing Units (GPUs), is also proposed for efficient computation of the shortest geodesic paths between all cortical surface point pairs. Based on experimental results, a mathematical model for the morphological variability and regularity of the cortical folding patterns in the vicinity of the DICCCOL sites is proposed. It is envisioned that this model could be potentially applied in several human brain image registration and brain mapping applications

Prospective life cycle assessment targeting the dual-carbon strategy: Progress review and methodological framework

In order to achieve the “dual carbon” goals, it is of great significance to evaluate the potential environmental impact of emerging technologies and make decisions in the early stages of technological development to reduce energy consumption and environmental impact throughout the entire life cycle of these technologies. Life cycle assessment can analyze the potential environmental impact of a product or service throughout its life cycle. Prospective life cycle assessment has been proposed to evaluate emerging technologies that are uncommercialized. This study analyzed relevant definitions of life cycle assessment, reviewed articles on prospective life cycle assessment both in China and internationally, and used bibliometric methods to explore the status of prospective life cycle assessment research in terms of research trends, objects, and key fields. Considering the four challenges associated with the method, including low comparability, nonlinear scale amplification, limited data availability, and high uncertainty, a comprehensive framework for prospective life cycle assessment has been proposed. This article also discussed the difficulties and challenges of using prospective life cycle assessment in research in China and provides suggestions for future improvement and application prospects. This study provides a reference for the localization application of the method in China, help the development of carbon reduction technology innovation, and coordinate pollution reduction. It can also help us identify product and supply chain hotspots from a systematic perspective, adopt targeted adjustments, avoid the transfer of environmental pollution burden, and achieve the “dual carbon” goals with a lower cost and environmentally friendly path

Design and implementation of multi-signal and time-varying neural reconstructions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Several efficient procedures exist to digitally trace neuronal structure from light microscopy, and mature community resources have emerged to store, share, and analyze these datasets. In contrast, the quantification of intracellular distributions and morphological dynamics is not yet standardized. Current widespread descriptions of neuron morphology are static and inadequate for subcellular characterizations. We introduce a new file format to represent multichannel information as well as an open-source Vaa3D plugin to acquire this type of data. Next we define a novel data structure to capture morphological dynamics, and demonstrate its application to different time-lapse experiments. Importantly, we designed both innovations as judicious extensions of the classic SWC format, thus ensuring full back-compatibility with popular visualization and modeling tools. We then deploy the combined multichannel/time-varying reconstruction system on developing neurons in live Drosophila larvae by digitally tracing fluorescently labeled cytoskeletal components along with overall dendritic morphology as they changed over time. This same design is also suitable for quantifying dendritic calcium dynamics and tracking arbor-wide movement of any subcellular substrate of interest.Peer reviewe

Aryl hydrocarbon receptor (AHR)-regulated transcriptomic changes in rats sensitive or resistant to major dioxin toxicities

Peer reviewe

In Situ Carbon Coated LiNi 0.5

Carbon coated spinel LiNi0.5Mn1.5O4 were prepared by spray-drying using prepolymer of melamine formaldehyde resin (PMF) as carbon source of carbon coating layer. The PMF carbon coated LiNi0.5Mn1.5O4 was characterized by XRD, SEM, and other electrochemical measurements. The as-prepared lithium nickel manganese oxide has the cubic face-centered spinel structure with a space group of Fd3m. It showed good electrochemical performance as a cathode material for lithium ion battery. After 100 discharge and charge cycles at 0.5 C rate, the specific discharge capacity of carbon coated LiNi0.5Mn1.5O4 was 130 mAh·g−1, and the corresponding capacity retention was 98.8%. The 100th cycle specific discharge capacity at 10 C rate of carbon coated LiNi0.5Mn1.5O4 was 105.4 mAh·g−1, and even the corresponding capacity retention was 95.2%