Thank you for your contribution brother to what is to come in the near future. 2020-present is incredibly concerning to the Hierarchy Watchers below regarding how humans are behaving in the Age of “freewill” Combination Therapies: Pisces & Aquarius. Until humans can control “beast inside of them”, then the literal door will be unlocked since the Collective has chosen to not open the door when Yeshua/Jesus Christ knocked...A good Father does discipline ALL his Creation/Children eventually...
Hi CVF team, is there any way I can work with you on my company's industrial AD project? We'd love to discuss this further if you are interested. Thanks before.
- Berton Earnshaw: "Microscopy, foundation models, and the scaling hypothesis: a phenomenal step forward for image-based profiling" • Foundation models are revolutionizing microscopy image analysis • The scaling hypothesis is being successfully applied to image-based profiling • These advancements are leading to significant improvements in microscopy techniques - Stephen K. Burley: "Protein Data Bank: From Two Epidemics to the Global Pandemic to mRNA Vaccines and Paxlovid" • The Protein Data Bank played a crucial role in understanding and addressing COVID-19 • It contributed significantly to the development of mRNA vaccines • The database was instrumental in the creation of antiviral treatments like Paxlovid - Christopher J Soelistyo and Alan R Lowe: "Discovering interpretable models of scientific image data with deep learning" • Presents a novel method for creating interpretable deep learning models • Focuses on analyzing scientific image data • Improves the transparency and understanding of AI decision-making in scientific contexts - Ali Nasiri-Sarvi et al.: "Vim4Path: Self-Supervised State Space Modeling for Histopathology Images" • Introduces a self-supervised state space model for histopathology image analysis • Improves the efficiency of analyzing complex histopathological data • Reduces the need for large amounts of labeled training data - Björn Möller et al.: "Low-Resolution-Only Microscopy Super-Resolution Models Generalizing to Non-Periodicities at Atomic Scale" • Develops super-resolution models that work with low-resolution microscopy images • Generalizes to non-periodic structures at the atomic scale • Enhances the capabilities of microscopy analysis in challenging scenarios - Paula A. Marin Zapata: "Learning and using self-supervised phenotypic features in small molecule discovery" • Applies self-supervised learning techniques to small molecule drug discovery • Enhances the identification of relevant phenotypic features • Accelerates the process of drug discovery and development - Alexander Sauer et al.: "Refining Biologically Inconsistent Segmentation Masks with Masked Autoencoders" • Uses masked autoencoders to improve segmentation masks in microscopy images • Addresses biological inconsistencies in image segmentation • Enhances the accuracy of microscopy image analysis - Andrey Ignatov et al.: "Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks" • Develops a deep neural network architecture aware of cell morphology • Improves the classification of histopathological images • Enhances the accuracy of disease diagnosis and prognosis - Siqi Liu: "Building Large-Scale Foundation Models for Digital Pathology with Millions of Whole Slides and Multi-Modal Generative AI: from Virchow to PRISM" • Discusses the development of large-scale foundation models for digital pathology • Incorporates millions of whole slide images in the analysis • Utilizes multi-modal generative AI techniques to advance pathology research - Mary D Aiyetigbo et al.: "Unsupervised Microscopy Video Denoising" • Presents an unsupervised method for denoising microscopy videos • Improves the quality of microscopy video data • Enhances the ability to analyze dynamic cellular processes - Sai Kumar Reddy Manne et al.: "NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer" • Introduces a nuclei-aware method for osteoclast instance segmentation • Enables knowledge transfer from mouse to human domains • Improves the analysis of bone-related diseases across species - Juyoung Yun et al.: "Uncertainty Estimation for Tumor Prediction with Unlabeled Data" • Presents a method for estimating uncertainty in tumor predictions • Utilizes unlabeled data to improve prediction accuracy • Enhances the reliability of cancer diagnosis and prognosis - Gan Gao et al.: "Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments" • Introduces a method for triaging 3D pathology data using 2.5D multiple-instance learning • Assists pathologists in their assessments of complex 3D data • Improves the efficiency and accuracy of pathological diagnoses - Charlotte Bunne: "Predicting Patient Treatment Outcomes using (Diffusion) Generative Models" • Discusses the use of diffusion-based generative models in predicting treatment outcomes • Enhances personalized medicine approaches • Improves the accuracy of treatment selection and patient care - Mahtab Bigverdi et al.: "GRAPE: GANs as Robust Adversarial Perturbation Encoders" • Presents a method using GANs as robust encoders for adversarial perturbations • Improves the resilience of microscopy image analysis to adversarial attacks • Enhances the reliability of AI-based microscopy analysis - Cheng Jiang et al.: "Super-resolution of biomedical volumes with 2D supervision" • Introduces a super-resolution technique for biomedical volume data • Requires only 2D supervision, reducing the need for complex 3D annotations • Improves the resolution and detail of 3D biomedical imaging - Heming Yao et al.: "Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images" • Presents a weakly supervised learning method for single-cell image analysis • Improves morphological profiling through set-consistency • Enhances the accuracy of single-cell phenotyping with limited labeled data - Vivek Gopalakrishnan et al.: "Grad-CAMO: Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images" • Introduces a method for learning interpretable morphological profiles from 3D cell painting images • Uses gradient-based class activation mapping for improved interpretability • Enhances the understanding of cellular morphology in 3D environments - Hanchuang Peng: "High-throughput mapping of 3D reconstructed neurons at whole-brain scale using petavoxel-computing" • Discusses techniques for high-throughput mapping of 3D reconstructed neurons • Applies advanced computing methods to whole-brain scale analysis • Advances our understanding of brain structure and function at unprecedented scales
