Reviewerly/exHarmony huggingface dataset:
Application of constraint-based frequent closed itemsets Mining in TCM Clinical data Analysis

Objective: To propose a frequent closed itemsets mining method based on item constraints to study the combination rules between symptoms, diagnoses, and herbs in clinical data of TCM. Methods: Based on the Charm algorithm, prune the itemsets which do not meet the constraint conditions firstly, design a constraint based frequent closed itemsets mining algorithm, which was used on the clinical dataset of TCM to confirm the effectiveness of the method and the clinical significance of the mining results. Results: Based on the Charm algorithm, a constraint based frequent closed itemsets mining algorithm was proposed. The results of the method for mining frequent closed itemsets from data of symptom, diagnosis and herbs were consistent with the relevant theories of TCM.Conclusion: The combination of constraint conditions and frequent closed itemsets mining methods can effectively reduce the mining space. Using constraint based frequent closed itemsets mining methods to mine the combination rules between symptoms, diagnosis, and herbs in TCM clinical data maybe helpful for assisting clinical diagnosis and treatment.
Source Free Domain Adaptation Based on Latent Dirichlet Allocation

Source-data free domain adaptation (SFDA) is from unsupervised domain adaptation (UDA) and applied to the special situations in reality that the source domain data is not accessible. To this end, a common method is self-supervision with marking pseudo labels on the data in target domain. While the method is simple and effective, error hard labels degenerate the final model seriously. In this paper, we introduce the latent dirichlet allocation (LDA) algorithm to enhance the predictive ability of the target model. We consider all the sample features within a neighborhood follow the same distribution. The prediction from the target model is viewed as a prior distribution of the sample. And we improve the prediction with pseudo-labels of samples in its neighborhood with the help of LDA. Compared with the baseline SHOT-IM, the experiment results show that our method is improved 1.3% and 1.6% on datasets Office-31 and Office-Home respectively.
Long Video Micro-expression Spotting Based On OCC Theory

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos.
Unsupervised Feature Selection by Fusing Spectral Clustering and Locality Preserving Projection

Due to feature redundancy in high dimensional data, the unsupervised feature selection methods for dimension reduction have attracted considerable attention. The current feature selection frameworks consider the global information of data, but ignore mutual screening of global and local information, and there have been no important breakthroughs on this approach research recently. We propose a novel feature selection method based on iterative optimization between the pseudo label matrix from spectral clustering and the local projection information (SNUFS), and then prove the convergence of the method. The pseudo label matrix and the local projection are designed in objective function for mutual screening and guiding the regularization feature selection by iterative approach. Our method selects features most relevant to the pseudo label and preserves the local structure of original data from feature selection matrix, where alternate iteration of different optimization items including pseudo label matrix achieve mutual screening. For this method, we give the objective function, iterative optimization fusion approach and convergence analysis in detail. Furthermore, we use K-Nearest Neighbor (KNN) and K-means to implement locality preserving projection and obtain two specific algorithms. Experiments on four real-world datasets in different fields demonstrate that our algorithms can effectively improve the accuracy of feature selection. In particular, our algorithm of KNN implementation is more effective and outperforms other major algorithms.
A Hounsfiled Unit Grayscale Adaptive Segmentation Network for Kidney Tumor Surgery

Computed tomography (CT)-based structural analysis of the kidney plays a crucial role in kidney tumor surgery. However, existing medical image segmentation algorithms still require improvements due to challenges associated with grayscale conversion and the influence of multi-organ tissues. To address these issues, we propose a multi-label grayscale adaptive segmentation network. Our method incorporates three key innovations: (1) We propose an algorithm to fit original CT data, dynamically adjusting window width via gradient coefficients. This enhances focus on detailed grayscale ranges. (2) We design an feature reorganization module to amalgamate features from diverse sensory fields. This facilitates addressing morphological changes without significantly increasing model parameters. (3) Our intermediate supervision approach enables gradient computation for direct feature extraction from various scales. It supervises model parameter training based on multi-level scaling labels, effectively minimizing computational errors. Experimental results showcase an average Dice similarity coefficient (DSC) surpassing 83.99% in Kipa2022. These outcomes validate the rapid and effective performance of the proposed model, underscoring its clinical significance in kidney tumor treatment.
FACExplainer: Generating Model-faithful Explanations for Graph Neural Networks Guided by Spatial Information

Graph neural networks (GNNs) have been widely applied in various decision-crucial fields, where accurate predictions with high interpretability are desired. Thus, numerous post-hoc explainers for GNNs have been proposed. However, some prioritize human-intelligible explanations through graph rules, such as the connection rule, which undermines the explanation’s faithfulness to the model. This paper proposes an innovative method, FACExplainer, that re-examines the role of spatial information within GNNs for generating model-faithful explanations. FACExplainer employs activation maps from the last graph convolution to narrow down a compact search space. Our approach further identifies the subgraph that maximizes mutual information as the explanation, eliminating the need for domain-specific knowledge about the downstream task. Empirical analysis of FACExplainer on seven benchmark datasets with three classical GNNs reveals significantly improved explanation quality while consuming less time when compared to leading explainers. The source code of FACExplainer is freely available at https://github.com/HuaYangttt/Facexplainer/.
Improving Medical Image Denoising via a Lightweight Plug-and-play Module

Medical image denoising, as a part of medical image processing, is significant for the assessment and diagnosis of diseases. To improve the medical image denoising performance of existing deep learning methods, we propose a lightweight plug-and-play module (LP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M) with low complexity, which can be plugged before current image denoising methods. Specifically, the proposed LP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M contains three stacked Convolutional Neural Network (CNN) based blocks: a image receptor block, an adaptive receptive field selection block and a high-low frequency processing block. The image receptor block can perceive color or grayscale images and perform preliminary processing. The adaptive receptive field selection block includes two parallel paths with different receptive fields (i.e., convolution kernel sizes) and the adaptive weighting operation, which can process multi-scale information in the image. The high-low frequency processing block consists of a low frequency pathway using convolutional layers with large kernel sizes, and a high frequency pathway using covolutional layers with small kernel sizes, which can process the low and high frequency components in images. Extensive validation experiments are performed on five state-of-the-art denoising methods on multiple medical image datasets for three different medical image denoising tasks (X-ray image denoising, magnetic resonance image denoising and dermoscopic image denoising). Experimental results show that our proposed LP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M can effectively improve the results of these five state-of-the-art methods for three denoising tasks with only increasing 0.996K parameters and 63.112M FLOPs, and it can provide potential direction for improving image denoising.
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (free-text) radiology reports. Our experimental results demonstrate the model’s efficacy, achieving an impressive 97% accuracy and 97% F1 score in predicting DVT, and an outstanding 98.3% accuracy and 98.4% F1 score in predicting PE. These findings emphasize the model’s robustness and its potential to significantly contribute to VTE research.
TS-DDD: A Two-Stage Training Strategy for Dialogue-based Disease Diagnosis

Dialogue-based automatic disease diagnosis has the potential to assist medical professionals in enhancing the accuracy and efficiency of diagnosis and treatment. Nonetheless, prior models grounded in reinforcement learning often exhibit a tendency to inquire about unrelated symptoms that hold limited relevance to the available symptom information within the constrained number of conversational exchanges. Consequently, these models may hastily assign a diagnosis to the patient. To address this issue, our paper draws inspiration from the diagnostic procedures employed by real-world physicians and introduces a novel two-stage training strategy termed TS-DDD for Dialogue-based Disease Diagnosis. In the initial stage, we pretrain two classifiers for symptom inquiry and disease prediction, separately. In the second stage, we devise a joint training framework that seamlessly integrates symptom inquiry, disease prediction, and decision-making processes. Extensive experiments are conducted and the results validate the effectiveness of our proposed TS-DDD model.
AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model

Continual pretraining is a popular way of building a domain-specific pretrained language model from a general-domain language model. In spite of its high efficiency, continual pretraining suffers from catastrophic forgetting, which may harm the model’s performance in downstream tasks. To alleviate the issue, in this paper, we propose a continual pretraining method for the BERT-based model, named Attention-FFN Adapter. Its main idea is to introduce a small number of attention heads and hidden units inside each self-attention layer and feed-forward network. Furthermore, we train a domain-specific language model named AF Adapter based RoBERTa for the Chinese biomedical domain. In experiments, models are applied to downstream tasks for evaluation. The results demonstrate that with only about 17% of model parameters trained, AF Adapter achieves 0.6%, 2% gain in performance on average, compared to strong baselines. Further experimental results show that our method alleviates the catastrophic forgetting problem by 11% compared to the fine-tuning method. Code is available at https://github.com/yanyongyu/AF-Adapter.
Diffusion-Enhanced Magnified Histopathological Images for Robust Breast Cancer Classification

Histopathological Image Analysis (HIA) is essential for breast cancer diagnosis, where classifier accuracy and precision are crucial for patient care. While developing a breast cancer classifier, researchers faced data limitations and the need for superior histopathological image discrimination. Complex cancerous tissues require nuanced and detailed analysis, but diverse and representative datasets are scarce. This study proposes a naive solution using diffusion techniques to propose a robust cancer classification method (Diffusion-enhanced BCCNet) with an efficient image enhancement mechanism. The three public Breast Cancer datasets (BreakHist, BHI Breast Histopathology Images, and BACH) have shown significant progress, demonstrating the potential of our proposed methodology to address data availability issues.
CCBox: Improving Box-supervised Nuclei Segmentation with Consistency Constraint

Nuclei segmentation is a critical step in the automated analysis of digitized microscopic images, which facilitates analysis of pathological images. The current state-of-the-art (SOTA) methods for nuclei segmentation require significant time and resources to provide pixel-level annotations for training. To reduce the labor-intensive annotation cost, we propose CCBox, a high-quality nuclei segmentation network that only requires bounding box annotations. We first generate hard pseudo labels for each nuclei within the bounding boxes using the traditional methods and then train a nuclei segmentation network with these hard pseudo labels. The major challenge lies in the presence of significant noise in the boundary regions of the nuclei due to the susceptibility of traditional methods to complex texture information in pathological images, impacting the model’s segmentation performance at the nucleus boundaries. To address this challenge, we propose the consistency constraint for similarity maps (CSM) strategy, which aggregates pixel features of the same semantics and enhances the discriminability of foreground and background features at the nucleus boundaries. Furthermore, to mitigate the overfitting of the model to noisy samples in the hard pseudo labels, we employ the hard-soft joint supervision strategy to supervise the student network. Extensive experiment results demonstrate that our CCBox significantly narrows the gap between box-supervised and fully-supervised nuclei segmentation methods.
Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation

Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts and utilizes unlabeled data which is much easier to acquire. Among existing perturbed consistency learning methods, mean-teacher model serves as a standard baseline for semi-supervised medical image segmentation. In this paper, we present a simple yet efficient competitive ensembling teacher student framework for semi-supervised for left atrium segmentation from 3D MR images, in which two student models with different task-level disturbances are introduced to learn mutually, while a competitive ensembling strategy is performed to ensemble more reliable information to teacher model. Different from the one-way transfer between teacher and student models, our framework facilitates the collaborative learning procedure of different student models with the guidance of teacher model and motivates different training networks for a competitive learning and ensembling procedure to achieve better performance. We evaluate our proposed method on the public Left Atrium (LA) dataset and it obtains impressive performance gains by exploiting the unlabeled data effectively and outperforms several existing semi-supervised methods.
MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients. However, most prognostic methods are currently limited to either histopathology or genomics alone, which inevitably reduces their potential to accurately predict patient prognosis. Whereas integrating WSIs and genomic features presents three main challenges: (1) the enormous heterogeneity of gigapixel WSIs which can reach sizes as large as 150,000×150,000 pixels; (2) the absence of a spatially corresponding relationship between histopathology images and genomic molecular data; and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture the explicit interactions between WSIs and genomics. To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment. To validate the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel WSIs across five different cancer types sourced from The Cancer Genome Atlas (TCGA). Extensive experimental results consistently emphasize that MGCT outperforms the state-of-the-art (SOTA) methods.
Towards Trustworthy Collaborative Healthcare Data Sharing

The integration of healthcare and data-driven technologies offers remarkable opportunities for medical research and patient care. However, it is crucial to adhere to the ethical responsibility of protecting patient data and maintaining robust privacy standards as the primary concern. Federated learning (FL) has been recognized as a promising technique to address this issue. FL allows multiple healthcare providers or institutions to collaboratively train machine learning models without the necessity of directly exchanging sensitive patient data. Nevertheless, using conventional FL methods, which rely extensively on centralized aggregators, poses significant challenges within healthcare, including privacy vulnerabilities, regulatory compliance, and the potential for malicious exploits. In order to address the above challenges, this paper presents the Cross-Silo Federated Learning Framework with Blockchain and Differential Privacy (CSFL-BDP), a framework solution designed to enhance the privacy and security of data in healthcare systems. The CSFL-BDP framework addresses these concerns by enabling collaborative model training across healthcare institutions while obviating the need for centralized aggregation. By leveraging blockchain technology, the CSFL-BDP framework ensures data integrity, immutability, decentralized control, and patient records protection. Furthermore, it integrates differential privacy techniques to protect sensitive medical information during collaborative model training. This framework has the potential to transform healthcare analytics in practical settings, enabling institutions to collaboratively enhance medical knowledge while maintaining patient data security and privacy.
Integrating Multimodal Contrastive Learning and Cross-Modal Attention for Alzheimer’s Disease Prediction in Brain Imaging Genetics

High annotation costs serve as a significant hurdle in deploying modern deep learning architectures for clinically relevant medical applications, especially when dealing with the inherent heterogeneity of multimodal data, proving the critical need for innovative algorithms that can effectively utilize unlabeled data. In this paper, we propose a model named MCLCA, which integrates multimodal contrastive learning and cross-modal attention to diagnose Alzheimer’s Disease (AD) and identify biomarkers using both labeled and unlabeled multimodal brain imaging genetics data. Through multimodal contrastive learning, MCLCA can effectively learn representations even in the absence of sufficient labels. By utilizing cross-modal attention blocks, the model captures deep connections between different modalities, providing a more comprehensive view of diagnosis. Our proposed MCLCA model is evaluated using the ADNI database with three imaging modalities (VBM-MRI, FDG-PET, and AV45-PET) and genetic SNP data. The results demonstrate that MCLCA can identify important biomarkers with better prediction accuracy compared to the existing methods. The source code is available at https://github.com/MCLCA.
Cervical Cytology Classification with Coarse Labels Based on Two-Stage Weakly Supervised Contrastive Learning Framework

Deep learning methods have achieved remarkable success in various tasks from cervical cytology images. However, for the gigapixel whole slide images (WSIs), the acquisition of annotations is a time-consuming and labor-intensive task requiring a high level of expertise. While the sparse distribution of malignant cells and the factors above pose great difficulties to label thousands of patches divided from the WSI, it is much easier to obtain the coarse labels at the WSI level. In this paper, we propose a novel weakly supervised contrastive learning framework, which utilizes only coarse labels from the WSIs for cervical cytology patch classification. The proposed framework consists of two stages, including the representation learning stage and the classifier finetuning stage. In the first stage, to effectively exploit useful information of coarse labels, we devise a re-weight cross-entropy loss, which can fast warm up the training and reduce the inexact supervision from the coarse labels simultaneously. To further excavate features bypassing the coarse labels, we propose a self-supervised contrastive loss, where the random augmentation and the mean teacher architecture enrich the external variations, and help better extract representations through patch similarities. In the second stage, based on ensemble predictions and uncertainty selections, reliable pseudo labels are generated for the inaccurate labels to finetune the classifier, with better performance achieved. Extensive experiments on the in-house dataset demonstrate that the proposed method is more efficient than other state-of-the-art methods. Our code is available on https://github.com/chaisiyii/WSCL.