In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival.
As a fundamental tool for natural language processing (NLP), the part-of-speech (POS) tagger assigns the POS label to each word in a sentence. A novel lightweight POS tagger based on word embeddings is proposed and named GWPT (green word-embedding-based POS tagger) in this work. Following the green learning (GL) methodology, GWPT contains three modules in cascade: 1) representation learning, 2) feature learning, and 3) decision learning modules. The main novelty of GWPT lies in representation learning. It uses non-contextual or contextual word embeddings, partitions embedding dimension indices into low-, medium-, and high-frequency sets, and represents them with different N-grams. It is shown by experimental results that GWPT offers state-of-the-art accuracies with fewer model parameters and significantly lower computational complexity in both training and inference as compared with deep-learning-based methods.
AI algorithms at the edge demand smaller model sizes and lower computational complexity. To achieve these objectives, we adopt a green learning (GL) paradigm rather than the deep learning paradigm. GL has three modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) supervised decision learning. We focus on the second module in this work. In particular, we derive new discriminant features from proper linear combinations of input features, denoted by x, obtained in the first module. They are called complementary and raw features, respectively. Along this line, we present a novel supervised learning method to generate highly discriminant complementary features based on the least-squares normal transform (LNT). LNT consists of two steps. First, we convert a C-class classification problem to a binary classification problem. The two classes are assigned with 0 and 1, respectively. Next, we formulate a least-squares regression problem from the N-dimensional (N-D) feature space to the 1-D output space, and solve the least-squares normal equation to obtain one N-D normal vector, denoted by a1. Since one normal vector is yielded by one binary split, we can obtain M normal vectors with M splits. Then, Ax is called an LNT of x, where transform matrix A in R^{M by N} by stacking aj^T, j=1, ..., M, and the LNT, Ax, can generate M new features. The newly generated complementary features are shown to be more discriminant than the raw features. Experiments show that the classification performance can be improved by these new features.
Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its semantics. One challenge is that different semantic statistics in source and target domains result in content discrepancy known as semantic distortion. To address this problem, a novel I2I method that maintains semantic consistency in translation is proposed and named SemST in this work. SemST reduces semantic distortion by employing contrastive learning and aligning the structural and textural properties of input and output by maximizing their mutual information. Furthermore, a multi-scale approach is introduced to enhance translation performance, thereby enabling the applicability of SemST to domain adaptation in high-resolution images. Experiments show that SemST effectively mitigates semantic distortion and achieves state-of-the-art performance. Also, the application of SemST to domain adaptation (DA) is explored. It is demonstrated by preliminary experiments that SemST can be utilized as a beneficial pre-training for the semantic segmentation task.
Many mathematical models have been leveraged to design embeddings for representing Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks. These mathematically-inspired models are not only highly scalable for inference in large KGs, but also have many explainable advantages in modeling different relation patterns that can be validated through both formal proofs and empirical results. In this paper, we make a comprehensive overview of the current state of research in KG completion. In particular, we focus on two main branches of KG embedding (KGE) design: 1) distance-based methods and 2) semantic matching-based methods. We discover the connections between recently proposed models and present an underlying trend that might help researchers invent novel and more effective models. Next, we delve into CompoundE and CompoundE3D, which draw inspiration from 2D and 3D affine operations, respectively. They encompass a broad spectrum of techniques including distance-based and semantic-based methods. We will also discuss an emerging approach for KG completion which leverages pre-trained language models (PLMs) and textual descriptions of entities and relations and offer insights into the integration of KGE embedding methods with PLMs for KG completion.
Supervised trackers trained on labeled data dominate the single object tracking field for superior tracking accuracy. The labeling cost and the huge computational complexity hinder their applications on edge devices. Unsupervised learning methods have also been investigated to reduce the labeling cost but their complexity remains high. Aiming at lightweight high-performance tracking, feasibility without offline pre-training, and algorithmic transparency, we propose a new single object tracking method, called the green object tracker (GOT), in this work. GOT conducts an ensemble of three prediction branches for robust box tracking: 1) a global object-based correlator to predict the object location roughly, 2) a local patch-based correlator to build temporal correlations of small spatial units, and 3) a superpixel-based segmentator to exploit the spatial information of the target frame. GOT offers competitive tracking accuracy with state-of-the-art unsupervised trackers, which demand heavy offline pre-training, at a lower computation cost. GOT has a tiny model size (<3k parameters) and low inference complexity (around 58M FLOPs per frame). Since its inference complexity is between 0.1%-10% of DL trackers, it can be easily deployed on mobile and edge devices.
Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed.
Knowledge graph entity typing (KGET) is a task to predict the missing entity types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to solve the KGET task by introducing an auxiliary relation, 'hasType', to model the relationship between entities and their types. However, a single auxiliary relation has limited expressiveness for diverse entity-type patterns. We improve the expressiveness of KGE methods by introducing multiple auxiliary relations in this work. Similar entity types are grouped to reduce the number of auxiliary relations and improve their capability to model entity-type patterns with different granularities. With the presence of multiple auxiliary relations, we propose a method adopting an Asynchronous learning scheme for Entity Typing, named AsyncET, which updates the entity and type embeddings alternatively to keep the learned entity embedding up-to-date and informative for entity type prediction. Experiments are conducted on two commonly used KGET datasets to show that the performance of KGE methods on the KGET task can be substantially improved by the proposed multiple auxiliary relations and asynchronous embedding learning. Furthermore, our method has a significant advantage over state-of-the-art methods in model sizes and time complexity.
In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H\&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.
Owing to the proliferation of user-generated videos on the Internet, blind video quality assessment (BVQA) at the edge attracts growing attention. The usage of deep-learning-based methods is restricted by their large model sizes and high computational complexity. In light of this, a novel lightweight BVQA method called GreenBVQA is proposed in this work. GreenBVQA features a small model size, low computational complexity, and high performance. Its processing pipeline includes: video data cropping, unsupervised representation generation, supervised feature selection, and mean-opinion-score (MOS) regression and ensembles. We conduct experimental evaluations on three BVQA datasets and show that GreenBVQA can offer state-of-the-art performance in PLCC and SROCC metrics while demanding significantly smaller model sizes and lower computational complexity. Thus, GreenBVQA is well-suited for edge devices.