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1,133 results on '"Zhou, Teng"'

1. TwinDiffusion: Enhancing Coherence and Efficiency in Panoramic Image Generation with Diffusion Models

Author

Zhou, Teng and Tang, Yongchuan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion models have emerged as effective tools for generating diverse and high-quality content. However, their capability in high-resolution image generation, particularly for panoramic images, still faces challenges such as visible seams and incoherent transitions. In this paper, we propose TwinDiffusion, an optimized framework designed to address these challenges through two key innovations: the Crop Fusion for quality enhancement and the Cross Sampling for efficiency optimization. We introduce a training-free optimizing stage to refine the similarity of adjacent image areas, as well as an interleaving sampling strategy to yield dynamic patches during the cropping process. A comprehensive evaluation is conducted to compare TwinDiffusion with the prior works, considering factors including coherence, fidelity, compatibility, and efficiency. The results demonstrate the superior performance of our approach in generating seamless and coherent panoramas, setting a new standard in quality and efficiency for panoramic image generation.

Published
2024

2. SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D Human Reconstruction

Author

Wang, Fei, Tang, Kongzhang, Wu, Hefeng, Zhao, Baoquan, Cai, Hao, and Zhou, Teng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Reconstructing 3D human shapes from 2D images has received increasing attention recently due to its fundamental support for many high-level 3D applications. Compared with natural images, freehand sketches are much more flexible to depict various shapes, providing a high potential and valuable way for 3D human reconstruction. However, such a task is highly challenging. The sparse abstract characteristics of sketches add severe difficulties, such as arbitrariness, inaccuracy, and lacking image details, to the already badly ill-posed problem of 2D-to-3D reconstruction. Although current methods have achieved great success in reconstructing 3D human bodies from a single-view image, they do not work well on freehand sketches. In this paper, we propose a novel sketch-driven multi-faceted decoder network termed SketchBodyNet to address this task. Specifically, the network consists of a backbone and three separate attention decoder branches, where a multi-head self-attention module is exploited in each decoder to obtain enhanced features, followed by a multi-layer perceptron. The multi-faceted decoders aim to predict the camera, shape, and pose parameters, respectively, which are then associated with the SMPL model to reconstruct the corresponding 3D human mesh. In learning, existing 3D meshes are projected via the camera parameters into 2D synthetic sketches with joints, which are combined with the freehand sketches to optimize the model. To verify our method, we collect a large-scale dataset of about 26k freehand sketches and their corresponding 3D meshes containing various poses of human bodies from 14 different angles. Extensive experimental results demonstrate our SketchBodyNet achieves superior performance in reconstructing 3D human meshes from freehand sketches., Comment: 9 pages, to appear in Pacific Graphics 2023

Published
2023

7. Comutations and KRASG12C Inhibitor Efficacy in Advanced NSCLC.

Author

Negrao, Marcelo, Araujo, Haniel, Lamberti, Giuseppe, Cooper, Alissa, Akhave, Neal, Zhou, Teng, Delasos, Lukas, Hicks, J, Aldea, Mihaela, Minuti, Gabriele, Hines, Jacobi, Aredo, Jacqueline, Dennis, Michael, Scott, Susan, Bironzo, Paolo, Scheffler, Matthias, Christopoulos, Petros, Stenzinger, Albrecht, Riess, Jonathan, Kim, So, Goldberg, Sarah, Li, Mingjia, Wang, Qi, Qing, Yun, Ni, Ying, Do, Minh, Lee, Richard, Ricciuti, Biagio, Alessi, Joao, Wang, Jing, Resuli, Blerina, Landi, Lorenza, Tseng, Shu-Chi, Nishino, Mizuki, Digumarthy, Subba, Rinsurongkawong, Waree, Rinsurongkawong, Vadeerat, Vaporciyan, Ara, Blumenschein, George, Zhang, Jianjun, Owen, Dwight, Mountzios, Giannis, Shu, Catherine, Bestvina, Christine, Garassino, Marina, Marrone, Kristen, Gray, Jhanelle, Patel, Sandip, Cummings, Amy, Wakelee, Heather, Wolf, Juergen, Scagliotti, Giorgio, Cappuzzo, Federico, Barlesi, Fabrice, Patil, Pradnya, Drusbosky, Leylah, Gibbons, Don, Meric-Bernstam, Funda, Lee, J, Heymach, John, Hong, David, Heist, Rebecca, Awad, Mark, Skoulidis, Ferdinandos, Blakely, Collin, and Chakrabarti, Turja

Subjects
Humans, Carcinoma, Non-Small-Cell Lung, Lung Neoplasms, Kelch-Like ECH-Associated Protein 1, Phosphatidylinositol 3-Kinases, Mutation, NF-E2-Related Factor 2, DNA Helicases, Nuclear Proteins, Transcription Factors
Abstract

UNLABELLED: Molecular modifiers of KRASG12C inhibitor (KRASG12Ci) efficacy in advanced KRASG12C-mutant NSCLC are poorly defined. In a large unbiased clinicogenomic analysis of 424 patients with non-small cell lung cancer (NSCLC), we identified and validated coalterations in KEAP1, SMARCA4, and CDKN2A as major independent determinants of inferior clinical outcomes with KRASG12Ci monotherapy. Collectively, comutations in these three tumor suppressor genes segregated patients into distinct prognostic subgroups and captured ∼50% of those with early disease progression (progression-free survival ≤3 months) with KRASG12Ci. Pathway-level integration of less prevalent coalterations in functionally related genes nominated PI3K/AKT/MTOR pathway and additional baseline RAS gene alterations, including amplifications, as candidate drivers of inferior outcomes with KRASG12Ci, and revealed a possible association between defective DNA damage response/repair and improved KRASG12Ci efficacy. Our findings propose a framework for patient stratification and clinical outcome prediction in KRASG12C-mutant NSCLC that can inform rational selection and appropriate tailoring of emerging combination therapies. SIGNIFICANCE: In this work, we identify co-occurring genomic alterations in KEAP1, SMARCA4, and CDKN2A as independent determinants of poor clinical outcomes with KRASG12Ci monotherapy in advanced NSCLC, and we propose a framework for patient stratification and treatment personalization based on the comutational status of individual tumors. See related commentary by Heng et al., p. 1513. This article is highlighted in the In This Issue feature, p. 1501.

Published
2023

10. The latest progress of breast cancer treatment at 2023 ESMO

Author

ZHOU Teng, ZHANG Jian

Subjects
breast cancer treatment, immune checkpoint inhibitor, cyclin-dependent kinase 4/6 inhibitors, antibody-drug conjugate, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

At 2023 Annual Meeting of European Society of Medical Oncology (ESMO), Several latest research achievements in the field of breast cancer were announced. For early-stage breast cancer, immune checkpoint inhibitor has not only continued to prove their value in the neoadjuvant treatment of triple-negative breast cancer (KEYNOTE-522 and NeoTRIP), but also shown their potential benefit in the neoadjuvant therapy of hormone receptor+/human epidermal growth factor receptor 2 (HER2)- breast cancer (KEYNOTE-756 and CheckMate 7FL). Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors (as shown in the MONARCHE and NATALEE studies) have also solidified an important position in the adjuvant intensive treatment of hormone receptor+/HER2- breast cancer. In advanced breast cancer treatment, new endocrine therapy options and antibody-drug conjugate (T-DXd and Dato-DXd) have opened up new therapeutic opportunities for hormone receptor+/HER2- patients. Pooled analyses suggest that T-DXd has demonstrated outstanding efficacy in brain metastases of HER2+ breast cancer. Dato-DXd in combination with immunization and the new targeted antibody-drug conjugate HS-20089 produced promising efficacy in first-line treatment of and post-line triple-negative breast cancer, respectively.

Published
2023
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29. Robust Exclusive Adaptive Sparse Feature Selection for Biomarker Discovery and Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus

Author

Quan, Tianhong, Yuan, Ye, Luo, Yu, Zhou, Teng, Qin, Jing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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37. CNN in CT Image Segmentation: Beyound Loss Function for Expoliting Ground Truth Images

Author

Song, Youyi, Yu, Zhen, Zhou, Teng, Teoh, Jeremy Yuen-Chun, Lei, Baiying, Choi, Kup-Sze, and Qin, Jing

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Exploiting more information from ground truth (GT) images now is a new research direction for further improving CNN's performance in CT image segmentation. Previous methods focus on devising the loss function for fulfilling such a purpose. However, it is rather difficult to devise a general and optimization-friendly loss function. We here present a novel and practical method that exploits GT images beyond the loss function. Our insight is that feature maps of two CNNs trained respectively on GT and CT images should be similar on some metric space, because they both are used to describe the same objects for the same purpose. We hence exploit GT images by enforcing such two CNNs' feature maps to be consistent. We assess the proposed method on two data sets, and compare its performance to several competitive methods. Extensive experimental results show that the proposed method is effective, outperforming all the compared methods., Comment: 4 pages, 3 figures, and having been accepted by ISBI 2020

Published
2020

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