NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga-pixel size necessitates computational methods based on multiple instance learning, which typically analyze small, isolated image tiles without the spatial context of the TME. To address this limitation and incorporate TME spatial histology interactions in predicting NAC response for TNBC patients, we developed a histology context-aware transformer graph convolution network (NACNet). Our deep learning method identifies the histopathological labels on individual image tiles from WSIs, constructs a spatial TME graph, and represents each node with features derived from tissue texture and social network analysis. It predicts NAC response using a transformer graph convolution network model enhanced with graph isomorphism network layers. We evaluate our method with WSIs of a cohort of TNBC patient (N=105) and compared its performance with multiple state-of-the-art machine learning and deep learning models, including both graph and non-graph approaches. Our NACNet achieves 90.0% accuracy, 96.0% sensitivity, 88.0% specificity, and an AUC of 0.82, through eight-fold cross-validation, outperforming baseline models. These comprehensive experimental results suggest that NACNet holds strong potential for stratifying TNBC patients by NAC response, thereby helping to prevent overtreatment, improve patient quality of life, reduce treatment cost, and enhance clinical outcomes, marking an important advancement toward personalized breast cancer treatment.

Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification

Radio Frequency Fingerprint Identification (RFFI) technology uniquely identifies emitters by analyzing unique distortions in the transmitted signal caused by non-ideal hardware. Recently, RFFI based on deep learning methods has gained popularity and is seen as a promising way to address the device authentication problem for Internet of Things (IoT) systems. However, in cross-receiver scenarios, where the RFFI model is trained over RF signals from some receivers but deployed at a new receiver, the alteration of receivers' characteristics would lead to data distribution shift and cause significant performance degradation at the new receiver. To address this problem, we first perform a theoretical analysis of the cross-receiver generalization error bound and propose a sufficient condition, named Separable Condition (SC), to minimize the classification error probability on the new receiver. Guided by the SC, a Receiver-Independent Emitter Identification (RIEI)model is devised to decouple the received signals into emitter-related features and receiver-related features and only the emitter-related features are used for identification. Furthermore, by leveraging federated learning, we also develop a FedRIEI model to eliminate the need for centralized collection of raw data from multiple receivers. Experiments on two real-world datasets demonstrate the superiority of our proposed methods over some baseline methods.

DACAT: Dual-stream Adaptive Clip-aware Time Modeling for Robust Online Surgical Phase Recognition

Surgical phase recognition has become a crucial requirement in laparoscopic surgery, enabling various clinical applications like surgical risk forecasting. Current methods typically identify the surgical phase using individual frame-wise embeddings as the fundamental unit for time modeling. However, this approach is overly sensitive to current observations, often resulting in discontinuous and erroneous predictions within a complete surgical phase. In this paper, we propose DACAT, a novel dual-stream model that adaptively learns clip-aware context information to enhance the temporal relationship. In one stream, DACAT pretrains a frame encoder, caching all historical frame-wise features. In the other stream, DACAT fine-tunes a new frame encoder to extract the frame-wise feature at the current moment. Additionally, a max clip-response read-out (Max-R) module is introduced to bridge the two streams by using the current frame-wise feature to adaptively fetch the most relevant past clip from the feature cache. The clip-aware context feature is then encoded via cross-attention between the current frame and its fetched adaptive clip, and further utilized to enhance the time modeling for accurate online surgical phase recognition. The benchmark results on three public datasets, i.e., Cholec80, M2CAI16, and AutoLaparo, demonstrate the superiority of our proposed DACAT over existing state-of-the-art methods, with improvements in Jaccard scores of at least 4.5%, 4.6%, and 2.7%, respectively. Our code and models have been released at https://github.com/kk42yy/DACAT.

Towards Robust Uncertainty-Aware Incomplete Multi-View Classification

Handling incomplete data in multi-view classification is challenging, especially when traditional imputation methods introduce biases that compromise uncertainty estimation. Existing Evidential Deep Learning (EDL) based approaches attempt to address these issues, but they often struggle with conflicting evidence due to the limitations of the Dempster-Shafer combination rule, leading to unreliable decisions. To address these challenges, we propose the Alternating Progressive Learning Network (APLN), specifically designed to enhance EDL-based methods in incomplete MVC scenarios. Our approach mitigates bias from corrupted observed data by first applying coarse imputation, followed by mapping the data to a latent space. In this latent space, we progressively learn an evidence distribution aligned with the target domain, incorporating uncertainty considerations through EDL. Additionally, we introduce a conflict-aware Dempster-Shafer combination rule (DSCR) to better handle conflicting evidence. By sampling from the learned distribution, we optimize the latent representations of missing views, reducing bias and enhancing decision-making robustness. Extensive experiments demonstrate that APLN, combined with DSCR, significantly outperforms traditional methods, particularly in environments characterized by high uncertainty and conflicting evidence, establishing it as a promising solution for incomplete multi-view classification.

Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor Segmentation

Multi-modal brain tumor segmentation typically involves four magnetic resonance imaging (MRI) modalities, while incomplete modalities significantly degrade performance. Existing solutions employ explicit or implicit modality adaptation, aligning features across modalities or learning a fused feature robust to modality incompleteness. They share a common goal of encouraging each modality to express both itself and the others. However, the two expression abilities are entangled as a whole in a seamless feature space, resulting in prohibitive learning burdens. In this paper, we propose DeMoSeg to enhance the modality adaptation by Decoupling the task of representing the ego and other Modalities for robust incomplete multi-modal Segmentation. The decoupling is super lightweight by simply using two convolutions to map each modality onto four feature sub-spaces. The first sub-space expresses itself (Self-feature), while the remaining sub-spaces substitute for other modalities (Mutual-features). The Self- and Mutual-features interactively guide each other through a carefully-designed Channel-wised Sparse Self-Attention (CSSA). After that, a Radiologist-mimic Cross-modality expression Relationships (RCR) is introduced to have available modalities provide Self-feature and also `lend' their Mutual-features to compensate for the absent ones by exploiting the clinical prior knowledge. The benchmark results on BraTS2020, BraTS2018 and BraTS2015 verify the DeMoSeg's superiority thanks to the alleviated modality adaptation difficulty. Concretely, for BraTS2020, DeMoSeg increases Dice by at least 0.92%, 2.95% and 4.95% on whole tumor, tumor core and enhanced tumor regions, respectively, compared to other state-of-the-arts. Codes are at https://github.com/kk42yy/DeMoSeg

A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot

Traditional license plate detection and recognition models are often trained on closed datasets, limiting their ability to handle the diverse license plate formats across different regions. The emergence of large-scale pre-trained models has shown exceptional generalization capabilities, enabling few-shot and zero-shot learning. We propose OneShotLP, a training-free framework for video-based license plate detection and recognition, leveraging these advanced models. Starting with the license plate position in the first video frame, our method tracks this position across subsequent frames using a point tracking module, creating a trajectory of prompts. These prompts are input into a segmentation module that uses a promptable large segmentation model to generate local masks of the license plate regions. The segmented areas are then processed by multimodal large language models (MLLMs) for accurate license plate recognition. OneShotLP offers significant advantages, including the ability to function effectively without extensive training data and adaptability to various license plate styles. Experimental results on UFPR-ALPR and SSIG-SegPlate datasets demonstrate the superior accuracy of our approach compared to traditional methods. This highlights the potential of leveraging pre-trained models for diverse real-world applications in intelligent transportation systems. The code is available at https://github.com/Dinghaoxuan/OneShotLP.

Prompt-Assisted Semantic Interference Cancellation on Moderate Interference Channels

The performance of conventional interference management strategies degrades when interference power is comparable to signal power. We consider a new perspective on interference management using semantic communication. Specifically, a multi-user semantic communication system is considered on moderate interference channels (ICs), for which a novel framework of deep learning-based prompt-assisted semantic interference cancellation (DeepPASIC) is proposed. Each transmitted signal is partitioned into common and private parts. The common parts of different users are transmitted simultaneously in a shared medium, resulting in superposition. The private part, on the other hand, serves as a prompt to assist in canceling the interference suffered by the common part at the semantic level. Simulation results demonstrate that the proposed DeepPASIC outperforms conventional interference management strategies under moderate interference conditions.

CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models

Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging and domain-specific task, such as finance, has not been fully explored. In this paper, we present CFinBench: a meticulously crafted, the most comprehensive evaluation benchmark to date, for assessing the financial knowledge of LLMs under Chinese context. In practice, to better align with the career trajectory of Chinese financial practitioners, we build a systematic evaluation from 4 first-level categories: (1) Financial Subject: whether LLMs can memorize the necessary basic knowledge of financial subjects, such as economics, statistics and auditing. (2) Financial Qualification: whether LLMs can obtain the needed financial qualified certifications, such as certified public accountant, securities qualification and banking qualification. (3) Financial Practice: whether LLMs can fulfill the practical financial jobs, such as tax consultant, junior accountant and securities analyst. (4) Financial Law: whether LLMs can meet the requirement of financial laws and regulations, such as tax law, insurance law and economic law. CFinBench comprises 99,100 questions spanning 43 second-level categories with 3 question types: single-choice, multiple-choice and judgment. We conduct extensive experiments of 50 representative LLMs with various model size on CFinBench. The results show that GPT4 and some Chinese-oriented models lead the benchmark, with the highest average accuracy being 60.16%, highlighting the challenge presented by CFinBench. The dataset and evaluation code are available at https://cfinbench.github.io/.

Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal

Remote sensing shadow removal, which aims to recover contaminated surface information, is tricky since shadows typically display overwhelmingly low illumination intensities. In contrast, the infrared image is robust toward significant light changes, providing visual clues complementary to the visible image. Nevertheless, the existing methods ignore the collaboration between heterogeneous modalities, leading to undesired quality degradation. To fill this gap, we propose a weakly supervised shadow removal network with a spherical feature space, dubbed S2-ShadowNet, to explore the best of both worlds for visible and infrared modalities. Specifically, we employ a modal translation (visible-to-infrared) model to learn the cross-domain mapping, thus generating realistic infrared samples. Then, Swin Transformer is utilized to extract strong representational visible/infrared features. Simultaneously, the extracted features are mapped to the smooth spherical manifold, which alleviates the domain shift through regularization. Well-designed similarity loss and orthogonality loss are embedded into the spherical space, prompting the separation of private visible/infrared features and the alignment of shared visible/infrared features through constraints on both representation content and orientation. Such a manner encourages implicit reciprocity between modalities, thus providing a novel insight into shadow removal. Notably, ground truth is not available in practice, thus S2-ShadowNet is trained by cropping shadow and shadow-free patches from the shadow image itself, avoiding stereotypical and strict pair data acquisition. More importantly, we contribute a large-scale weakly supervised shadow removal benchmark, including 4000 shadow images with corresponding shadow masks.

Cost-Effective RF Fingerprinting Based on Hybrid CVNN-RF Classifier with Automated Multi-Dimensional Early-Exit Strategy

While the Internet of Things (IoT) technology is booming and offers huge opportunities for information exchange, it also faces unprecedented security challenges. As an important complement to the physical layer security technologies for IoT, radio frequency fingerprinting (RFF) is of great interest due to its difficulty in counterfeiting. Recently, many machine learning (ML)-based RFF algorithms have emerged. In particular, deep learning (DL) has shown great benefits in automatically extracting complex and subtle features from raw data with high classification accuracy. However, DL algorithms face the computational cost problem as the difficulty of the RFF task and the size of the DNN have increased dramatically. To address the above challenge, this paper proposes a novel costeffective early-exit neural network consisting of a complex-valued neural network (CVNN) backbone with multiple random forest branches, called hybrid CVNN-RF. Unlike conventional studies that use a single fixed DL model to process all RF samples, our hybrid CVNN-RF considers differences in the recognition difficulty of RF samples and introduces an early-exit mechanism to dynamically process the samples. When processing "easy" samples that can be well classified with high confidence, the hybrid CVNN-RF can end early at the random forest branch to reduce computational cost. Conversely, subsequent network layers will be activated to ensure accuracy. To further improve the early-exit rate, an automated multi-dimensional early-exit strategy is proposed to achieve scheduling control from multiple dimensions within the network depth and classification category. Finally, our experiments on the public ADS-B dataset show that the proposed algorithm can reduce the computational cost by 83% while improving the accuracy by 1.6% under a classification task with 100 categories.