Deep learning architectures for 2D and 3D scene perception

Scene understanding is a fundamental problem in computer vision tasks, that is being more intensively explored in recent years with the development of deep learning. In this dissertation, we proposed deep learning structures to address challenges in 2D and 3D scene perception. We developed several novel architectures for 3D point cloud understanding at city-scale point by effectively capturing both long-range and short-range information to handle the challenging problem of large variations in object size for city-scale point cloud segmentation. GLSNet++ is a two-branch network for multiscale point cloud segmentation that models this complex problem using both global and local processing streams to capture different levels of contextual and structural 3D point cloud information. We developed PointGrad, a new graph convolution gradient operator for capturing structural relationships, that encoded point-based directional gradients into a high-dimensional multiscale tensor space. Using the Point- Grad operator with graph convolution on scattered irregular point sets captures the salient structural information in the point cloud across spatial and feature scale space, enabling efficient learning. We integrated PointGrad with several deep network architectures for large-scale 3D point cloud semantic segmentation, including indoor scene and object part segmentation. In many real application areas including remote sensing and aerial imaging, the class imbalance is common and sufficient data for rare classes is hard to acquire or has high-cost associated with expert labeling. We developed MDXNet for few-shot and zero-shot learning, which emulates the human visual system by leveraging multi-domain knowledge from general visual primitives with transfer learning for more specialized learning tasks in various application domains. We extended deep learning methods in various domains, including the material domain for predicting carbon nanotube forest attributes and mechanical properties, biomedical domain for cell segmentation.Includes bibliographical references

Accuracy of Segment-Anything Model (SAM) in medical image segmentation tasks

The segment-anything model (SAM), was introduced as a fundamental model for segmenting images. It was trained using over 1 billion masks from 11 million natural images. The model can perform zero-shot segmentation of images by using various prompts such as masks, boxes, and points. In this report, we explored (1) the accuracy of SAM on 12 public medical image segmentation datasets which cover various organs (brain, breast, chest, lung, skin, liver, bowel, pancreas, and prostate), image modalities (2D X-ray, histology, endoscropy, and 3D MRI and CT), and health conditions (normal, lesioned). (2) if the computer vision foundational segmentation model SAM can provide promising research directions for medical image segmentation. We found that SAM without re-training on medical images does not perform as accurately as U-Net or other deep learning models trained on medical images.Comment: Technical Repor

αV Integrin Induces Multicellular Radioresistance in Human Nasopharyngeal Carcinoma via Activating SAPK/JNK Pathway

BACKGROUND:Tumor cells acquire the capacity of resistance to chemotherapy or radiotherapy via cell-matrix and cell-cell crosstalk. Integrins are the most important cell adhesion molecules, in which αV integrin mainly mediating the tight contact between tumor cells. METHODOLOGY/PRINCIPAL FINDINGS:To investigate the role of αV integrin in multi-cellular radioresistance (MCR) of human nasopharyngeal carcinoma (NPC), we performed immunohistochemistry and Western blotting to find that the expression of αV integrin in the tumor tissue of radioresistant patients is much higher than that in radiosensitive patients. In vitro, we cultured human NPC cell line CNE-2 cells as multi-cellular spheroids (MCSs) or as monolayer cells (MCs), and found that the expression of αV integrin in MCSs is significantly higher than that in MCs. MTT, flow cytometry and clonogenic survival assays showed that MCSs are less sensitive to X-ray irradiation than MCs while blocking of αV integrin in MCSs dramatically reversed their radioresistance. Furthermore, as detected by Western blotting, MCSs displayed sustained activation of the stress-activated protein kinase/c-Jun NH2-terminal kinase (SAPK/JNK) pathway in presence of irradiation. Blocking of αV integrin in MCSs decreased the expression of phosphorylated JNK. Additionally, blocking of SAPK/JNK signaling pathway synergistically induced apoptosis of MCSs exposed to irradiation by increasing the expression of cleaved caspase-3. In vivo, we found that irradiation combined with αV integrin blocking treatment significantly enhanced the radiosensitivity of NPC xenografts. CONCLUSIONS:Our results indicate a novel role of αV integrin in multi-cellular radioresistance of NPCs

The Cell Tracking Challenge: 10 years of objective benchmarking

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a signifcant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.Web of Science2071020101

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A bite-sized guide to chinese business manners

When you step out of the airport, China hits you with the traffic, and the incredible quantities of people around. This guide will lead you to familiarize the main business manners in China with some detailed tips, because most often it is the small details that can make the best impression. These pieces of advice will help you to understand more about Chinese business manners, etiquette and culture. At the same time your business success will be in your own hands through your own insights and efforts

A bite-sized guide for Finnish SME companies operating in China

Finland and China have been enjoying a long-standing and traditional friendship. By 2012, about 300 Finnish enterprises have become established in China. These enterprises represent all major industries. This partnership has steadily expanded since China entered WTO in 2001. However, despite being a huge market rife with opportunities, China also poses quite a few challenges, especially for smaller companies. The main difficulties are related to the different economic system as well as unfamiliar business culture and communication. This report examines some characteristics and challenges, covering the topics related to human resources management, Chinese law and bureaucracy, working with the government and state officials, networking, building legitimacy as well as partnering with local firms

BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Manual Lesion Annotation

BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ∼ 5/1000 term neonates. Accurate identification and segmentation of HIE-related lesions in neonatal brain magnetic resonance images (MRIs) is the first step toward predicting prognosis, identifying high-risk patients, and evaluating treatment effects. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy. We release the first public dataset containing neonatal brain diffusion MRI and expert annotation of lesions from 133 patients diagnosed with HIE. HIE-related lesions in brain MRI are often diffuse (i.e., multi-focal), and small (over half the patients in our data having lesions occupying <1% of brain volume). Segmentation for HIE MRI data is remarkably different from, and arguably more challenging than, other segmentation tasks such as brain tumors with focal and relatively large lesions. We hope that this dataset can help fuel the development of MRI lesion segmentation methods for HIE and small diffuse lesions in general. Contact: Rina Bao: [email protected] Yangming Ou: [email protected] License: All training data has been made publicly available under the CC BY NC ND license (https://creativecommons.org/licenses/by-nc-nd/2.0/, allowing academic use with credit, prohibiting commercial use without owner’s permission, and disallowing derivation or adaption of data)

Geographical Detector Model for Influencing Factors of Industrial Sector Carbon Dioxide Emissions in Inner Mongolia, China

Studying the influencing factors of carbon dioxide emissions is not only practically but also theoretically crucial for establishing regional carbon-reduction policies, developing low-carbon economy and solving the climate problems. Therefore, we used a geographical detector model which is consists of four parts, i.e., risk detector, factor detector, ecological detector and interaction detector to analyze the effect of these social economic factors, i.e., GDP, industrial structure, urbanization rate, economic growth rate, population and road density on the increase of energy consumption carbon dioxide emissions in industrial sector in Inner Mongolia northeast of China. Thus, combining with the result of four detectors, we found that GDP and population more influence than economic growth rate, industrial structure, urbanization rate and road density. The interactive effect of any two influencing factors enhances the increase of the carbon dioxide emissions. The findings of this research have significant policy implications for regions like Inner Mongolia