Robust Core-Periphery Constrained Transformer for Domain Adaptation

Unsupervised domain adaptation (UDA) aims to learn transferable representation across domains. Recently a few UDA works have successfully applied Transformer-based methods and achieved state-of-the-art (SOTA) results. However, it remains challenging when there exists a large domain gap between the source and target domain. Inspired by humans' exceptional transferability abilities to adapt knowledge from familiar to uncharted domains, we try to apply the universally existing organizational structure in the human functional brain networks, i.e., the core-periphery principle to design the Transformer and improve its UDA performance. In this paper, we propose a novel brain-inspired robust core-periphery constrained transformer (RCCT) for unsupervised domain adaptation, which brings a large margin of performance improvement on various datasets. Specifically, in RCCT, the self-attention operation across image patches is rescheduled by an adaptively learned weighted graph with the Core-Periphery structure (CP graph), where the information communication and exchange between images patches are manipulated and controlled by the connection strength, i.e., edge weight of the learned weighted CP graph. Besides, since the data in domain adaptation tasks can be noisy, to improve the model robustness, we intentionally add perturbations to the patches in the latent space to ensure generating robust learned weighted core-periphery graphs. Extensive evaluations are conducted on several widely tested UDA benchmarks. Our proposed RCCT consistently performs best compared to existing works, including 88.3\% on Office-Home, 95.0\% on Office-31, 90.7\% on VisDA-2017, and 46.0\% on DomainNet.Comment: Core-Periphery, ViT, Unsupervised domain adaptatio

Exploring the Influence of Information Entropy Change in Learning Systems

In this work, we explore the influence of entropy change in deep learning systems by adding noise to the inputs/latent features. The applications in this paper focus on deep learning tasks within computer vision, but the proposed theory can be further applied to other fields. Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this paper aims to rethink whether the conventional proposition always holds. We demonstrate that specific noise can boost the performance of various deep architectures under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the complexity of the task. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we achieved an unprecedented top 1 accuracy of over 95% on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm for improving model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change.Comment: Information Entropy, CNN, Transforme

Core-Periphery Principle Guided Redesign of Self-Attention in Transformers

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques. Previous studies, by post-hoc analysis, have found that the best-performing ANNs surprisingly resemble biological neural networks (BNN), which indicates that ANNs and BNNs may share some common principles to achieve optimal performance in either machine learning or cognitive/behavior tasks. Inspired by this phenomenon, we proactively instill organizational principles of BNNs to guide the redesign of ANNs. We leverage the Core-Periphery (CP) organization, which is widely found in human brain networks, to guide the information communication mechanism in the self-attention of vision transformer (ViT) and name this novel framework as CP-ViT. In CP-ViT, the attention operation between nodes is defined by a sparse graph with a Core-Periphery structure (CP graph), where the core nodes are redesigned and reorganized to play an integrative role and serve as a center for other periphery nodes to exchange information. We evaluated the proposed CP-ViT on multiple public datasets, including medical image datasets (INbreast) and natural image datasets. Interestingly, by incorporating the BNN-derived principle (CP structure) into the redesign of ViT, our CP-ViT outperforms other state-of-the-art ANNs. In general, our work advances the state of the art in three aspects: 1) This work provides novel insights for brain-inspired AI: we can utilize the principles found in BNNs to guide and improve our ANN architecture design; 2) We show that there exist sweet spots of CP graphs that lead to CP-ViTs with significantly improved performance; and 3) The core nodes in CP-ViT correspond to task-related meaningful and important image patches, which can significantly enhance the interpretability of the trained deep model.Comment: Core-periphery, functional brain networks, Vi

Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification

With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern. Many approaches have been developed to tackle the problem through post-hoc analysis, such as explaining how predictions are made or understanding the meaning of neurons in middle layers. Nevertheless, these methods can only discover the patterns or rules that naturally exist in models. In this work, rather than relying on post-hoc schemes, we proactively instill knowledge to alter the representation of human-understandable concepts in hidden layers. Specifically, we use a hierarchical tree of semantic concepts to store the knowledge, which is leveraged to regularize the representations of image data instances while training deep models. The axes of the latent space are aligned with the semantic concepts, where the hierarchical relations between concepts are also preserved. Experiments on real-world image datasets show that our method improves model interpretability, showing better disentanglement of semantic concepts, without negatively affecting model classification performance

Segment Anything Model (SAM) for Radiation Oncology

In this study, we evaluate the performance of the Segment Anything Model (SAM) model in clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \& neck, which are typical treatment sites in radiation oncology. For each case, we selected the OARs of concern in radiotherapy planning and compared the Dice and Jaccard outcomes between clinical manual delineation, automatic segmentation using SAM's "segment anything" mode, and automatic segmentation using SAM with box prompt. Our results indicate that SAM performs better in automatic segmentation for the prostate and lung regions, while its performance in the gastrointestinal and head \& neck regions was relatively inferior. When considering the size of the organ and the clarity of its boundary, SAM displays better performance for larger organs with clear boundaries, such as the lung and liver, and worse for smaller organs with unclear boundaries, like the parotid and cochlea. These findings align with the generally accepted variations in difficulty level associated with manual delineation of different organs at different sites in clinical radiotherapy. Given that SAM, a single trained model, could handle the delineation of OARs in four regions, these results also demonstrate SAM's robust generalization capabilities in automatic segmentation for radiotherapy, i.e., achieving delineation of different radiotherapy OARs using a generic automatic segmentation model. SAM's generalization capabilities across different regions make it technically feasible to develop a generic model for automatic segmentation in radiotherapy

When Brain-inspired AI Meets AGI

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4's reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) GPT-4 outperforms ChatGPT in the radiology NLI task; 2) other specifically fine-tuned models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings demonstrate that constructing a generic model that is capable of solving various tasks across different domains is feasible

Surviving ChatGPT in healthcare

At the dawn of of Artificial General Intelligence (AGI), the emergence of large language models such as ChatGPT show promise in revolutionizing healthcare by improving patient care, expanding medical access, and optimizing clinical processes. However, their integration into healthcare systems requires careful consideration of potential risks, such as inaccurate medical advice, patient privacy violations, the creation of falsified documents or images, overreliance on AGI in medical education, and the perpetuation of biases. It is crucial to implement proper oversight and regulation to address these risks, ensuring the safe and effective incorporation of AGI technologies into healthcare systems. By acknowledging and mitigating these challenges, AGI can be harnessed to enhance patient care, medical knowledge, and healthcare processes, ultimately benefiting society as a whole

Generation of integration-free neural progenitor cells from cells in human urine

Human neural stem cells hold great promise for research and therapy in neural disease. We describe the generation of integration-free and expandable human neural progenitor cells (NPCs). We combined an episomal system to deliver reprogramming factors with a chemically defined culture medium to reprogram epithelial-like cells from human urine into NPCs (hUiNPCs). These transgene-free hUiNPCs can self-renew and can differentiate into multiple functional neuronal subtypes and glial cells in vitro. Although functional in vivo analysis is still needed, we report that the cells survive and differentiate upon transplant into newborn rat brain.postprin