\section{Related Works}
This section reviews the prior research in three key areas: (1) image classification in medical imaging, (2) multi-modal learning with DICOM-based datasets, and (3) contrastive and representation learning for clinical data. These domains collectively inform the development of our framework, which integrates classification and representation learning within a unified multi-modal architecture that fuses DICOM images, radiomics features, and patient-specific metadata.


\subsection{Classification in Medical Vision}
Image classification is a significant task in computer vision, typically involving the assignment of a label to an input image based on learned representations. In the context of medical imaging, convolutional neural networks (CNNs) have been highlighted as a central role since the introduction of architectures such as VGG~\cite{simonyan2015deepconvolutionalnetworkslargescale} and ResNet~\cite{he2015deepresiduallearningimage}, particularly following the success of ImageNet~\cite{5206848}. These foundational models have since been extended into a range of more efficient or lightweight variants, including DenseNet~\cite{huang2018denselyconnectedconvolutionalnetworks}, MobileNet~\cite{howard2017mobilenetsefficientconvolutionalneural}, SqueezeNet~\cite{iandola2016squeezenetalexnetlevelaccuracy50x}, SENet~\cite{hu2019squeezeandexcitationnetworks}, and EfficientNet~\cite{tan2020efficientnetrethinkingmodelscaling}.

More recently, transformer-based models have gained traction in medical vision, following the introduction of ViT~\cite{dosovitskiy2021imageworth16x16words}, which extended transformer architectures from natural language processing to visual recognition tasks. Hybrid designs such as ConvNeXt~\cite{liu2022convnet2020s} have further bridged CNNs and transformers by modifying ResNet to incorporate design principles from ViT. In terms of clinical application, classification models have been widely explored across various disease domains.

For rotator cuff tears (RCT), prior work has primarily focused on segmentation tasks~\cite{lee2023automated,kim2024development,lee2021imbalanced,lee2020wide}, with fewer studies addressing direct classification. CSS-Net~\cite{lee2022css} employed supervised learning to model the correlation between supraspinatus tendon features and tear pathology, demonstrating improved diagnostic accuracy. In breast cancer imaging, most research has emphasized segmentation-based pipelines~\cite{baccouche2021connected,yue2022deep}, with recent classification-focused efforts such as EDCNN~\cite{islam2024enhancing} integrating MobileNet~\cite{howard2017mobilenetsefficientconvolutionalneural} and Xception~\cite{chollet2017xceptiondeeplearningdepthwise} architectures, using U-Net as a segmentation backbone. Lee~\textit{et al.}~\cite{lee2023self} proposed a self-supervised domain adaptation method leveraging BUS~\cite{yap2017automated}, BUSI~\cite{al2020dataset}, and BUV~\cite{10.1007/978-3-031-16437-8_59} datasets, further improving diagnostic generalization for breast cancer. For obstructive sleep apnea (OSA), earlier approaches utilized rule-based and classical methods to detect apnea events~\cite{al2012automated,javed2015dynamic}, which have since evolved into CNN-based pipelines for diagnosis~\cite{dey2018obstructive,park2023osa}. Notably, Sheta~\textit{et al.}~\cite{sheta2023diagnosis} demonstrated that grouping patients by demographic attributes (e.g., age, gender, race) prior to feature selection yielded measurable improvements in classification performance. 

Further, numerous studies have explored CNN-based classification across a variety of medical datasets beyond disease-specific models~\cite{li2014medical,wang2017hospital,kermany2018identifying,yadav2019deep,vianna2018study,dai2021transmed,huang2023self,he2024open,ju2024universal,huang2024adapting}. These efforts continue to highlight the promise of image-based deep learning for disease detection and diagnosis in clinical settings.



% \subsection{Classification in Medical Vision}

% Image classification is the problem of how a computer classifies to which category an input image belongs, typically using a CNN.
% Since the advent of deep learning research and its application to computer vision, models based on basic CNN structures such as VGG~\cite{simonyan2015deepconvolutionalnetworkslargescale} and ResNet~\cite{he2015deepresiduallearningimage} have emerged, including ImageNet~\cite{5206848}, which pioneered the model of deep learning. In the following years, extended models based on these models were developed, such as DenseNet~\cite{huang2018denselyconnectedconvolutionalnetworks}, MobileNet~\cite{howard2017mobilenetsefficientconvolutionalneural}, SqueezeNet~\cite{iandola2016squeezenetalexnetlevelaccuracy50x}, SENet~\cite{hu2019squeezeandexcitationnetworks}, and EfficientNet~\cite{tan2020efficientnetrethinkingmodelscaling}.
% Later, when the emergence of transformers only affected the field of natural language processing, starting with the paper ViT~\cite{dosovitskiy2021imageworth16x16words}, the field of computer vision also began to study visual problems that used convolutional network structures as transformer structures. Of course, convolutional models are still under investigation, as is the case with the ConvNext model~\cite{liu2022convnet2020s}. Based on the structural design of ViT, ConvNext modified ResNet to test its limits with CNN.

% For the rotator cuff tears in our dataset, we found that prior studies~\cite{lee2023automated,kim2024development,lee2021imbalanced,lee2020wide} were mainly performed on the segmentation task and rarely on the classification task. In CSS-Net~\cite{lee2022css}, used supervised learning to learn the correlation between RCT and ST and improve the accuracy of the diagnosis. 
% Several previous studies have been conducted on the segmentation task using breast cancer datasets ~\cite{baccouche2021connected,yue2022deep}. Paper~\cite{islam2024enhancing} proposes an ensemble deep convolutional neural network (EDCNN)~\cite{Zhang_2017} model combining MobileNet~\cite{howard2017mobilenetsefficientconvolutionalneural} and Xception~\cite{chollet2017xceptiondeeplearningdepthwise}, and uses the U-Net segmentation technique to segment the input image. Paper~\cite{lee2023self} proposes and studies a self-supervised domain adaptation framework using the BUS~\cite{yap2017automated}, BUSI~\cite{al2020dataset}, and BUV~\cite{10.1007/978-3-031-16437-8_59} breast cancer datasets, which contributed to breast cancer diagnosis accuracy. In sleep apnea, studies using related data have shown that several algorithms can be combined to automatically recognize~\cite {al2012automated} or visualize and detect~\cite{javed2015dynamic} apnea syndromes during sleep, and this has extended to studies using CNN-based approaches to diagnose OSA~\cite{dey2018obstructive,park2023osa}.

% Particularly, the study~\cite{sheta2023diagnosis} addresses the diagnosis of OSA using feature selection and classification methods, grouping data by age, gender, and race, and demonstrates that some accuracy improvements were validated when grouping data and using feature selection approaches. Furthermore, classification studies using medical datasets have been proposed, ranging from a study proposing a customized CNN using ConvLayer~\cite{li2014medical} to classification studies using medical datasets to identify diseases that can be diagnosed or treated via image-based deep learning~\cite{wang2017hospital,kermany2018identifying,yadav2019deep,huang2023self,vianna2018study,he2024open,ju2024universal,huang2024adapting,dai2021transmed}.



\begin{figure*}[t!]
    \centering
    \includegraphics[width=0.85\linewidth]{figures/figure-01-overall.png}
\caption{Schematic overview of the Statistically Coherent Network (SCN). DICOM images and patient attributes are processed by multiple encoders and combined via convolution blocks. The resulting multi-space latent distributions are illustrated by the histograms, and the final decoder (Header) generates disease classifications. The supervision term ($L_1$) aligns predicted labels with ground-truth labels.}
\label{fig:architecture}
\end{figure*}


\subsection{DICOM for Multi-Modal Perspective}
To better exploit the rich information contained in DICOM images, recent research in medical imaging has increasingly adopted multi-modal learning strategies. These approaches aim to integrate diverse data sources, including raw imaging, radiomics, and clinical metadata, within unified learning frameworks. Key directions include the development of DICOM-specific preprocessing pipelines and modality translation models~\cite{anklesaria2022multi}, the design of AI systems capable of integrating multi-modal features~\cite{biomedinformatics3030034}, and the extraction of complementary radiological and pathological characteristics~\cite{10.3389/fonc.2024.1287995}. Broader studies have also investigated the overall impact of multi-modal AI in clinical imaging~\cite{simon2024future} and presented systematic meta-analyses~\cite{andrade2023multi}.

Multi-modal architectures have been especially impactful in dermatology, where skin lesion classification has benefited from the joint use of dermoscopic and clinical images~\cite{8333693,10.1007/978-3-319-66179-7_29,https://doi.org/10.1111/exd.13777,nunnari:hal-03414735,Nedelcu2020MultiDatasetTF,9543523,PPR:PPR403537}. Building on this, multi-modal image fusion methods have been proposed to combine heterogeneous modalities and improve model performance and generalizability~\cite{tang2022matr,team2024chameleon,zhou2024transfusion}. These fusion strategies have also been applied to multi-scale lesion detection (MSLD) tasks, as demonstrated by architectures such as FusionM4Net~\cite{tang2022fusionm4net} and TFormer~\cite{zhang2023tformer}. In radiology, multi-modal deep learning has emerged as a dominant paradigm. Studies~\cite{kuang2024multimodal,cai2024multimodal,10.3389/fmed.2025.1635819,s22176594} have shown that integrating DICOM images with structured patient information and radiological annotations can significantly enhance the robustness and clinical relevance of predictive models. These advances underscore the potential of multi-modal fusion in building comprehensive, patient-aware diagnostic systems.



% \subsection{Dicom for Multi-Modal perspective}
% To better utilize DICOM's characteristics, many researchers in the medical field are studying how to apply multi-modal learning to conduct research, such as designing DICOM data preprocessing methods and multi-modal image conversion models~\cite{anklesaria2022multi}, AI-based models that integrate multi-modal data~\cite{biomedinformatics3030034} and extract radiology and pathology features~\cite{10.3389/fonc.2024.1287995}, analyzing the potential impact of multi-modal AI in medical imaging~\cite{simon2024future}, and presenting meta-analyses~\cite{andrade2023multi}.
% Particularly, multi-modality has been widely used in skin lesion diagnosis as a computer-aided technique based on deep convolution~\cite{8333693, 10.1007/978-3-319-66179-7_29, https://doi.org/10.1111/exd.13777, nunnari:hal-03414735, Nedelcu2020MultiDatasetTF, 9543523, PPR:PPR403537}. Following the proposal of multi-modal image fusion, a method integrating information from various modalities to improve model performance and generalization has been proposed\cite{tang2022matr,team2024chameleon,zhou2024transfusion}, and models that utilize it to perform MSLD have also been proposed\cite{tang2022fusionm4net,zhang2023tformer}. Furthermore, multi-modal deep learning has been the focus of considerable research in the radiology field, where the integration of image data with other modalities, such as clinical information and radiological features, has led to the proposal of comprehensive prediction models\cite{kuang2024multimodal, cai2024multimodal, 10.3389/fmed.2025.1635819, s22176594}.



% \subsection{Contrastive and Representative Learning}
% When performing computer vision tasks, it is also necessary to capture the intrinsic features of the data and learn about the data without annotations or labels in limited data or environments. Therefore, researchers are applying representation learning, a methodology via which machines extract key information from data and learn meaningful patterns. Among various approaches, contrastive learning—a branch of representation learning—focuses on capturing similarities and differences between data representations via comparative analysis of input samples~\cite{wang2021understanding}. One commonly used one is triplet loss, which compares a baseline anchor to a positive and negative, and Facenet~\cite{schroff2015facenet} uses it to generate embedding vectors directly from images. Representative contrastive learning frameworks include SimCLR~\cite{chen2020simple}, and SimCLRv2~\cite{chen2020big}-which complement SimCLR-, MoCo~\cite{he2020momentum}, BYOL~\cite{grill2020bootstrap}, and others, which are widely studied, such as understanding how contrastive learning works~\cite{wang2020understanding}, evaluating the quality of representation learning, or utilizing contrast learning to use it for visual representation~\cite{liu2023improving}.


\subsection{Contrastive and Representation Learning}

In computer vision tasks, especially under limited annotation settings, it is crucial to capture the intrinsic structure of data through unsupervised or self-supervised learning techniques. Representation learning offers a general framework through which models can extract meaningful patterns without relying on explicit labels. Among these, contrastive learning has emerged as a powerful paradigm that learns discriminative embeddings by comparing samples in a pairwise or triplet setting~\cite{wang2021understanding}. A commonly used technique is triplet loss, where an anchor sample is contrasted against a positive (similar) and a negative (dissimilar) instance. This formulation was popularized by FaceNet~\cite{schroff2015facenet}, which learns embedding vectors directly from facial images. More broadly, several contrastive learning frameworks have demonstrated strong performance in visual representation learning, including SimCLR~\cite{chen2020simple}, SimCLRv2~\cite{chen2020big}, MoCo~\cite{he2020momentum}, and BYOL~\cite{grill2020bootstrap}. These methods differ in whether they use memory banks, momentum encoders, or prediction heads, but all share the core principle of contrasting positive and negative samples to shape the latent space. Recent work has explored theoretical perspectives~\cite{wang2020understanding}, embedding alignment and uniformity, and downstream performance of learned representations~\cite{liu2023improving}. These findings highlight the potential of contrastive objectives to improve generalization, particularly in medical imaging tasks with heterogeneous or small-scale datasets. Our work builds on this foundation by introducing a statistically guided contrastive objective tailored for clinical subgroup alignment.

