1<!DOCTYPE html> 2<html lang="en"> 3<!-- Template: https://github.com/luost26/academic-homepage --> 4<head> 5 <meta charset="utf-8"> 6 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> 7 <title>Publications - Ying Chen</title> 8 9 <!-- Stylesheets --> 10 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.6.0/css/bootstrap.min.css" integrity="sha512-P5MgMn1jBN01asBgU0z60Qk4QxiXo86+wlFahKrsQf37c9cro517WzVSPPV1tDKzhku2iJ2FVgL67wG03SGnNA==" crossorigin="anonymous" /> 11 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.1/css/all.min.css" integrity="sha512-DTOQO9RWCH3ppGqcWaEA1BIZOC6xxalwEsw9c2QQeAIftl+Vegovlnee1c9QX4TctnWMn13TZye+giMm8e2LwA==" crossorigin="anonymous"> 12 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/academicons/1.9.1/css/academicons.min.css" integrity="sha512-b1ASx0WHgVFL5ZQhTgiPWX+68KjS38Jk87jg7pe+qC7q9YkEtFq0z7xCglv7qGIs/68d3mAp+StfC8WKC5SSAg==" crossorigin="anonymous" /> 13 <link rel="preconnect" href="https://fonts.googleapis.com"> 14 <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> 15 <link href="https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,300;0,400;0,700;0,900;1,300;1,400;1,700;1,900&family=Fira+Sans:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,800;1,900&family=Source+Code+Pro:ital,wght@0,200..900;1,200..900&display=swap" rel="stylesheet"> 16 <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"> 17 <link rel="stylesheet" href="/assets/css/global.css"> 18</head> 19<body class="bg-light" data-spy="scroll" data-target="#navbar-year" data-offset="100"> 20 <nav class="navbar navbar-expand-sm navbar-light fixed-top mb-5 shadow-sm"> 21 <div class="site-navbar-inner container-lg"> 22 <button class="navbar-toggler ml-auto" style="font-size: 1em; padding: 0.5em;" type="button" data-toggle="collapse" data-target="#navbarResponsive" aria-controls="navbarResponsive" aria-expanded="false" aria-label="Toggle navigation"> 23 <i class="fas fa-map"></i> Menu 24 </button> 25 26 <div class="collapse navbar-collapse" id="navbarResponsive"> 27 <ul class="site-navbar-links navbar-nav mx-auto"> 28 29 <li class="nav-item "> 30 <a class="nav-link" href="/">Home</a> 31 </li> 32 33 <li class="nav-item active"> 34 <a class="nav-link" href="/publications">Publications</a> 35 </li> 36 37 <li class="nav-item "> 38 <a class="nav-link" href="/showcase">Showcase</a> 39 </li> 40 41 </ul> 42 </div> 43 </div> 44</nav> 45 46 <div class="container-lg"> 47 48 49<div class="row"> 50 <div class="col-12 col-lg-10"> 51 52 53 <h2 class="pt-4" id="year-2026">2026</h2> 54 <div class="my-0 p-0 bg-white shadow-sm rounded-xl"> 55 56 57<div class="d-none d-md-block"> 58 <div class="row no-gutters border-bottom border-gray"> 59 <div class="col-md-3 col-xl-2 mb-md-0 p-md-3"><img data-src="/assets/images/covers/slidechat-nature-cancer.png" alt="SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types" class="lazy w-100 rounded-sm" src="/assets/images/empty_300x200.png"></div> 60 <div class="col-md-9 col-xl-10 p-3 pl-md-0"> 61 <h5 class="mt-0 mb-1 font-weight-normal">SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types</h5> 62 <p class="mt-0 mb-0 small"><span class="text-body"> 63 <strong>Ying Chen*</strong>, </span><span class="text-body"> 64 Chenglong Ma*, </span><span class="text-body"> 65 Qiongqiong Li, </span><span class="text-body"> 66 Fang Yan, </span><span class="text-body"> 67 Yirong Chen, </span><span class="text-body"> 68 Tianbin Li, </span><span class="text-body"> 69 Jin Ye, </span><span class="text-body"> 70 Ming Hu, </span><span class="text-body"> 71 Yuxiang Lin, </span><span class="text-body"> 72 Yanjun Li, </span><span class="text-body"> 73 Guoan Wang, </span><span class="text-body"> 74 Huihui Xu, </span><span class="text-body"> 75 Hui Dong, </span><span class="text-body"> 76 Xiang Wang, </span><span class="text-body"> 77 Xiaoxiao Xu, </span><span class="text-body"> 78 Yanyan Zhou, </span><span class="text-body"> 79 Xia Zhu, </span><span class="text-body"> 80 Sen Yang, </span><span class="text-body"> 81 Xiyue Wang, </span><span class="text-body"> 82 Lu Zhang, </span><span class="text-body"> 83 Yu Qiao, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu<sup>#</sup></a>
83, <span class="text-body"> 84 Junjun He<sup>#</sup>, </span><span class="text-body"> 85 Yuanfeng Ji<sup>#</sup></span> 86<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 87 <p class="mt-0 mb-0 small"><i>Nature Cancer</i> 2026 <span data-semantic-scholar-id=""></span></p> 88 <p class="mt-0 mb-0 small text-muted">SlideChat is a multimodal generative AI assistant for whole-slide pathology, supporting visual question answering and report generation across cancer types.</p> 89 90 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 91 92 93 <a target="_blank" href="/paper/SlideChat_NatCancer.pdf">[Paper]</a> 94 95 96 97 <a target="_blank" href="https://doi.org/10.1038/s43018-026-01220-4">[DOI]</a> 98 99 100 101 <a target="_blank" href="https://uni-medical.github.io/SlideChat.github.io">[Project]</a> 102 103 104 </p> 105 106 </div> 107 </div> 108</div> 109 110<div class="row no-gutters d-md-none border-bottom border-gray rounded-xl-top lazy" data-src="/assets/images/covers/slidechat-nature-cancer.png"> 111 <div class="w-100 rounded-xl-top " style="background-color: rgba(255,255,255,0.9);"> 112 <div class="d-flex align-items-start flex-column py-3 px-4"> 113 <div class="mb-auto"></div> 114 <div> 115 <h5 class="mt-0 mb-1 font-weight-normal">SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types</h5> 116 <p class="mt-0 mb-0 small"><span class="text-body"> 117 <strong>Ying Chen*</strong>, </span><span class="text-body"> 118 Chenglong Ma*, </span><span class="text-body"> 119 Qiongqiong Li, </span><span class="text-body"> 120 Fang Yan, </span><span class="text-body"> 121 Yirong Chen, </span><span class="text-body"> 122 Tianbin Li, </span><span class="text-body"> 123 Jin Ye, </span><span class="text-body"> 124 Ming Hu, </span><span class="text-body"> 125 Yuxiang Lin, </span><span class="text-body"> 126 Yanjun Li, </span><span class="text-body"> 127 Guoan Wang, </span><span class="text-body"> 128 Huihui Xu, </span><span class="text-body"> 129 Hui Dong, </span><span class="text-body"> 130 Xiang Wang, </span><span class="text-body"> 131 Xiaoxiao Xu, </span><span class="text-body"> 132 Yanyan Zhou, </span><span class="text-body"> 133 Xia Zhu, </span><span class="text-body"> 134 Sen Yang, </span><span class="text-body"> 135 Xiyue Wang, </span><span class="text-body"> 136 Lu Zhang, </span><span class="text-body"> 137 Yu Qiao, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu<sup>#</sup></a>, <span class="text-body"> 138 Junjun He<sup>#</sup>, </span><span class="text-body"> 139 Yuanfeng Ji<sup>#</sup></span> 140<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 141 <p class="mt-0 mb-0 small"><i>Nature Cancer</i> 2026 <span data-semantic-scholar-id=""></span></p> 142 <p class="mt-0 mb-0 small text-muted">SlideChat is a multimodal generative AI assistant for whole-slide pathology, supporting visual question answering and report generation across cancer types.</p> 143 144 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 145 146 147 <a target="_blank" href="/paper/SlideChat_NatCancer.pdf">[Paper]</a> 148 149 150 151 <a target="_blank" href="https://doi.org/10.1038/s43018-026-01220-4">[DOI]</a> 152 153 154 155 <a target="_blank" href="https://uni-medical.github.io/Sl
155ideChat.github.io">[Project]</a> 156 157 158 </p> 159 </div> 160 </div> 161 </div> 162 163</div> 164 165 166<div class="d-none d-md-block"> 167 <div class="row no-gutters border-gray"> 168 <div class="col-md-3 col-xl-2 mb-md-0 p-md-3"><img data-src="/assets/images/covers/biomtan.png" alt="BioMTAN: A Biological Knowledge-Guided Multi-Task Attention Network for Co-Enhanced Cancer Diagnosis and Prognosis" class="lazy w-100 rounded-sm" src="/assets/images/empty_300x200.png"></div> 169 <div class="col-md-9 col-xl-10 p-3 pl-md-0"> 170 <h5 class="mt-0 mb-1 font-weight-normal">BioMTAN: A Biological Knowledge-Guided Multi-Task Attention Network for Co-Enhanced Cancer Diagnosis and Prognosis</h5> 171 <p class="mt-0 mb-0 small"><span class="text-body"> 172 <strong>Ying Chen*</strong>, </span><span class="text-body"> 173 Jiajing Xie*, </span><span class="text-body"> 174 Yuxiang Lin, </span><span class="text-body"> 175 Yuhang Song, </span><span class="text-body"> 176 Wenxian Yang, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu<sup>#</sup></a> 177<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 178 <p class="mt-0 mb-0 small"><i>IEEE Journal of Biomedical and Health Informatics (JBHI)</i> 2026 <span data-semantic-scholar-id=""></span></p> 179 <p class="mt-0 mb-0 small text-muted">BioMTAN integrates biological pathway knowledge with multi-task attention to jointly predict cancer molecular subtypes and survival risk from gene expression data.</p> 180 181 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 182 183 184 <a target="_blank" href="/paper/BioMTAN_A_Biological_Knowledge-Guided_Multi-Task_Attention_Network_for_Co-Enhanced_Cancer_Diagnosis_and_Prognosis.pdf">[Paper]</a> 185 186 187 188 <a target="_blank" href="https://doi.org/10.1109/JBHI.2025.3638707">[DOI]</a> 189 190 191 </p> 192 193 </div> 194 </div> 195</div> 196 197<div class="row no-gutters d-md-none border-gray rounded-xl-bottom lazy" data-src="/assets/images/covers/biomtan.png"> 198 <div class="w-100 rounded-xl-bottom" style="background-color: rgba(255,255,255,0.9);"> 199 <div class="d-flex align-items-start flex-column py-3 px-4"> 200 <div class="mb-auto"></div> 201 <div> 202 <h5 class="mt-0 mb-1 font-weight-normal">BioMTAN: A Biological Knowledge-Guided Multi-Task Attention Network for Co-Enhanced Cancer Diagnosis and Prognosis</h5> 203 <p class="mt-0 mb-0 small"><span class="text-body"> 204 <strong>Ying Chen*</strong>, </span><span class="text-body"> 205 Jiajing Xie*, </span><span class="text-body"> 206 Yuxiang Lin, </span><span class="text-body"> 207 Yuhang Song, </span><span class="text-body"> 208 Wenxian Yang, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu<sup>#</sup></a> 209<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 210 <p class="mt-0 mb-0 small"><i>IEEE Journal of Biomedical and Health Informatics (JBHI)</i> 2026 <span data-semantic-scholar-id=""></span></p> 211 <p class="mt-0 mb-0 small text-muted">BioMTAN integrates biological pathway knowledge with multi-task attention to jointly predict cancer molecular subtypes and survival risk from gene expression data.</p> 212 213 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 214 215 216 <a target="_blank" href="/paper/BioMTAN_A_Biological_Knowledge-Guided_Multi-Task_Attention_Network_for_Co-Enhanced_Cancer_Diagnosis_and_Prognosis.pdf">[Paper]</a> 217 218 219 220 <a target="_blank" href="https://doi.org/10.1109/JBHI.2025.3638707">[DOI]</a> 221 222 223 </p> 224 </div> 225 </div> 226 </div> 227 228</div> 229 230 </div> 231 232 233 <h2 class="pt-4" id="year-2025">2025</h2> 234 <div class="my-0 p-0 bg-white shadow-sm rounded-xl"> 235 236 237<div class="d-none d-md-block"> 238 <div class="row no-gutters border-bottom border-gray"> 239 <div class="col-md-3 col-xl-2 mb-md-0 p-md-3"><img data-src="/assets/images/covers/survmamba.png" alt="SurvMamba: State Space Model with Multi-Grained Multi-Modal Interaction for Survival Prediction" class="lazy w-100 rounded-sm" src="/assets/images/empty_300x200.png"></div> 240 <div class="col-md-9 col-xl-10 p-3 pl-md-0"> 241 <h5 class="mt-0 mb-1 font-weight-normal">SurvMamba: State Space Model with Multi-Grained Multi-Modal Interaction for Survival Prediction</h5> 242 <p class="mt-0 mb-0 small"><span class="text-body"> 243 <strong>Ying Chen</strong>, </span><span class="text-body"> 244 Jiajing Xie, </span><span class="text-body"> 245 Yuxiang Lin, </span><span class="text-body"> 246 Yuhang Song, </span><span class="text-body"> 247 Chen Zhang, </span><span class="text-body"> 248 Wenxian Yang, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu<sup>#</sup></a> 249<mark>(<sup>#</sup> <i> corresponding author</i>)</mark></p> 250 <p class="mt-0 mb-0 small"><i>IEEE International Conference on Bioinformatics and Biomedicine (BIBM)</i> 2025 <span data-semantic-scholar-id=""></span></p> 251 <p class="mt-0 mb-0 small text-muted">SurvMamba introduces Mamba-based hierarchical intra-modal and inter-modal interaction modules to integrate whole-slide images and transcriptomic data for efficient cancer survival prediction.</p> 252 253 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 254 255 256 <a target="_blank" href="/paper/SurvMamba_State_Space_Model_with_Multi-Grained_Multi-Modal_Interaction_for_Survival_Prediction.pdf">[Paper]</a> 257 258 259 260 <a target="_blank" href="https://github.com/CYing18/SurvMamba">[Code]</a> 261 262 263 264 <a target="_blank" href="https://doi.org/10.1109/BIBM66473.2025.11356727">[DOI]</a> 265 266 267 </p> 268 269 </div> 270 </div> 271</div> 272 273<div class="row no-gutters d-md-none border-bottom border-gray rounded-xl-top lazy" data-src="/assets/images/covers/survmamba.png"> 274 <div class="w-100 rounded-xl-top " style="background-color: rgba(255,255,255,0.9);"> 275 <div class="d-flex align-items-start flex-column py-3 px-4"> 276 <div class="mb-auto"></div> 277 <div> 278 <h5 class="mt-0 mb-1 font-weight-normal">SurvMamba: State Space Model with Multi-Grained Multi-Modal Interaction for Survival Prediction</h5> 279 <p class="mt-0 mb-0 small"><span class="text-body"> 280 <strong>Ying Chen</strong>, </span><span class="text-body"> 281 Jiajing Xie, </span><span class="text-body"> 282 Yuxiang Lin, </span><span class="text-body"> 283 Yuhang Song, </span><span class="text-body"> 284 Chen Zhang, </span><span class="text-body"> 285 Wenxian Yang, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu<sup>#</sup></a> 286<mark>(<sup>#</sup> <i> corresponding author</i>)</mark></p> 287 <p class="mt-0 mb-0 small"><i>IEEE International Conference on Bioinformatics and Biomedicine (BIBM)</i> 2025 <span data-semantic-scholar-id=""></span></p> 288 <p class="mt-0 mb-0 small text-muted">SurvMamba introduces Mamba-based hierarchical intra-modal and inter-modal interaction modules to integrate whole-slide images and transcriptomic data for efficient cancer survival prediction.</p> 289 290 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 291 292 293 <a target="_blank" href="/paper/SurvMamba_State_Space_Model_with_Multi-Grained_Multi-Modal_Interaction_for_Survival_Prediction.pdf">[Paper]</a> 294 295 296 297 <a target="_blank" href="https://github.com/CYing18/SurvMamba">[Code]</a> 298 299 300 301 <a target="_blank" href="https://doi.org/10.1109/BIBM66473.2025.11356727">[DOI]</a> 302 303 304 </p> 305 </div> 306 </div> 307 </div> 308 309</div> 310 311 312<div class="d-none d-md-block"> 313 <div class="row no-gutters border-gray"> 314 <div class="col-md-3 col-xl-2 mb-md-0 p-md-3"><img data-src="/assets/images/covers/slidechat.png" alt="SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding" class="lazy w-100 rounded-sm" src="/assets/images/empty_300x200.png"></div> 315 <div class="col-md-9 col-xl-10 p-3 pl-md-0"> 316 <h5 class="mt-0 mb-1 font-weight-normal">SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding</h5> 317 <p class="mt-0 mb-0 small"><span class="text-body"> 318 <strong>Ying Chen*</strong>, </span><span class="text-body"> 319 Guoan Wang*, </span><span class="text-body"> 320 Yuanfeng Ji*<sup>#</sup>, </span><span class="text-body"> 321 Yanjun Li, </span><span class="text-body"> 322 Jin Ye, </span><span class="text-body"> 323 Tianbin Li, </span><span class="text-body"> 324 Ming Hu, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu</a>
324, <span class="text-body"> 325 Yu Qiao, </span><span class="text-body"> 326 Junjun He<sup>#</sup></span> 327<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 328 <p class="mt-0 mb-0 small"><i>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i> 2025 <span data-semantic-scholar-id=""></span></p> 329 <p class="mt-0 mb-0 small text-muted">SlideChat is an open-source vision-language assistant for gigapixel whole-slide pathology images, built with SlideInstruction and evaluated on SlideBench across captioning and VQA tasks.</p> 330 331 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 332 333 334 <a target="_blank" href="/paper/Chen_SlideChat_A_Large_Vision-Language_Assistant_for_Whole-Slide_Pathology_Image_Understanding_CVPR_2025_paper.pdf">[Paper]</a> 335 336 337 338 <a target="_blank" href="https://uni-medical.github.io/SlideChat.github.io">[Project]</a> 339 340 341 </p> 342 343 </div> 344 </div> 345</div> 346 347<div class="row no-gutters d-md-none border-gray rounded-xl-bottom lazy" data-src="/assets/images/covers/slidechat.png"> 348 <div class="w-100 rounded-xl-bottom" style="background-color: rgba(255,255,255,0.9);"> 349 <div class="d-flex align-items-start flex-column py-3 px-4"> 350 <div class="mb-auto"></div> 351 <div> 352 <h5 class="mt-0 mb-1 font-weight-normal">SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding</h5> 353 <p class="mt-0 mb-0 small"><span class="text-body"> 354 <strong>Ying Chen*</strong>, </span><span class="text-body"> 355 Guoan Wang*, </span><span class="text-body"> 356 Yuanfeng Ji*<sup>#</sup>, </span><span class="text-body"> 357 Yanjun Li, </span><span class="text-body"> 358 Jin Ye, </span><span class="text-body"> 359 Tianbin Li, </span><span class="text-body"> 360 Ming Hu, </span><a class="text-body" target="_blank" href="https://scholar.google.com/citations?user=Uh1EpKQAAAAJ">Rongshan Yu</a>, <span class="text-body"> 361 Yu Qiao, </span><span class="text-body"> 362 Junjun He<sup>#</sup></span> 363<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 364 <p class="mt-0 mb-0 small"><i>IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</i> 2025 <span data-semantic-scholar-id=""></span></p> 365 <p class="mt-0 mb-0 small text-muted">SlideChat is an open-source vision-language assistant for gigapixel whole-slide pathology images, built with SlideInstruction and evaluated on SlideBench across captioning and VQA tasks.</p> 366 367 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 368 369 370 <a target="_blank" href="/paper/Chen_SlideChat_A_Large_Vision-Language_Assistant_for_Whole-Slide_Pathology_Image_Understanding_CVPR_2025_paper.pdf">[Paper]</a> 371 372 373 374 <a target="_blank" href="https://uni-medical.github.io/SlideChat.github.io">[Project]</a> 375 376 377 </p> 378 </div> 379 </div> 380 </div> 381 382</div> 383 384 </div> 385 386 387 <h2 class="pt-4" id="year-2023">2023</h2> 388 <div class="my-0 p-0 bg-white shadow-sm rounded-xl"> 389 390 391<div class="d-none d-md-block"> 392 <div class="row no-gutters border-gray"> 393 <div class="col-md-3 col-xl-2 mb-md-0 p-md-3"><img data-src="/assets/images/covers/rafnet.png" alt="RAFNet: Restricted Attention Fusion Network for Sleep Apnea Detection" class="lazy w-100 rounded-sm" src="/assets/images/empty_300x200.png"></div> 394 <div class="col-md-9 col-xl-10 p-3 pl-md-0"> 395 <h5 class="mt-0 mb-1 font-weight-normal">RAFNet: Restricted Attention Fusion Network for Sleep Apnea Detection</h5> 396 <p class="mt-0 mb-0 small"><span class="text-body"> 397 <strong>Ying Chen*</strong>, </span><span class="text-body"> 398 Huijun Yue*, </span><span class="text-body"> 399 Ruifeng Zou, </span><span class="text-body"> 400 Wenbin Lei, </span><span class="text-body"> 401 Wenjun Ma, </span><span class="text-body"> 402 Xiaomao Fan<sup>#</sup></span> 403<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 404 <p class="mt-0 mb-0 small"><i>Neural Networks</i> 2023 <span data-semantic-scholar-id=""></span></p> 405 <p class="mt-0 mb-0 small text-muted">RAFNet detects sleep apnea from single-lead ECG by using restricted attention to fuse target and adjacent ECG segments while suppressing redundant neighboring information.</p> 406 407 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 408 409 410 <a target="_blank" href="/paper/RAFNet.pdf">[Paper]</a> 411 412 413 414 <a target="_blank" href="https://doi.org/10.1016/j.neunet.2023.03.019">[DOI]</a> 415 416 417 </p> 418 419 </div> 420 </div> 421</div> 422 423<div class="row no-gutters d-md-none border-gray rounded-xl-top rounded-xl-bottom lazy" data-src="/assets/images/covers/rafnet.png"> 424 <div class="w-100 rounded-xl-top rounded-xl-bottom" style="background-color: rgba(255,255,255,0.9);"> 425 <div class="d-flex align-items-start flex-column py-3 px-4"> 426 <div class="mb-auto"></div> 427 <div> 428 <h5 class="mt-0 mb-1 font-weight-normal">RAFNet: Restricted Attention Fusion Network for Sleep Apnea Detection</h5> 429 <p class="mt-0 mb-0 small"><span class="text-body"> 430 <strong>Ying Chen*</strong>, </span><span class="text-body"> 431 Huijun Yue*, </span><span class="text-body"> 432 Ruifeng Zou, </span><span class="text-body"> 433 Wenbin Lei, </span><span class="text-body"> 434 Wenjun Ma, </span><span class="text-body"> 435 Xiaomao Fan<sup>#</sup></span> 436<mark>(* <i> equal contribution</i>, <sup>#</sup> <i> corresponding author</i>)</mark></p> 437 <p class="mt-0 mb-0 small"><i>Neural Networks</i> 2023 <span data-semantic-scholar-id=""></span></p> 438 <p class="mt-0 mb-0 small text-muted">RAFNet detects sleep apnea from single-lead ECG by using restricted attention to fuse target and adjacent ECG segments while suppressing redundant neighboring information.</p> 439 440 <p class="small pb-0 mb-0 lh-125 text-muted abstract-links"> 441 442 443 <a target="_blank" href="/paper/RAFNet.pdf">[Paper]</a> 444 445 446 447 <a target="_blank" href="https://doi.org/10.1016/j.neunet.2023.03.019">[DOI]</a> 448 449 450 </p> 451 </div> 452 </div> 453 </div> 454 455</div> 456 457 </div> 458 459 </div> 460 461 <div class="col-2 d-none d-lg-block"> 462 <div id="navbar-year" class="nav nav-pills flex-column sticky-top" style="top: 80px"> 463 464 <a class="nav-link d-block" href="#year-2026">2026</a> 465 466 <a class="nav-link d-block" href="#year-2025">2025</a> 467 468 <a class="nav-link d-block" href="#year-2023">2023</a> 469 470 </div> 471 </div> 472 473</div> 474 475 </div> 476 <footer class="footer border-top py-2 mt-5 bg-white small"> 477 <div class="container-lg"> 478 <div class="row my-3"> 479 <div class="col-6"> 480 <div class="text-muted"> 481 <i>Last updated: Sep 2026</i> 482 </div> 483 </div> 484 <div class="col-6"> 485 <div class="text-right text-muted"> 486 <a href="https://github.com/luost26/academic-homepage" target="_blank"><i class="fas fa-pencil-ruler"></i> academic-homepage</a> 487 </div> 488 </div> 489 </div> 490 </div> 491</footer> 492 493 494 <!-- Scripts --> 495
495<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
495 496
496<script src="//cdnjs.cloudflare.com/ajax/libs/jquery.lazy/1.7.9/jquery.lazy.min.js"></script>
496 497
497<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1" crossorigin="anonymous"></script>
497 498
498<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.6.0/js/bootstrap.min.js" integrity="sha512-XKa9Hemdy1Ui3KSGgJdgMyYlUg1gM+QhL6cnlyTe2qzMCYm4nAZ1PsVerQzTTXzonUR+dmswHqgJPuwCq1MaAg==" crossorigin="anonymous"></script>
498 499
499<script src="https://cdnjs.cloudflare.com/ajax/libs/github-buttons/2.14.2/buttons.min.js" integrity="sha512-OYwZx04hKFeFNYrWxIyo3atgGpb+cxU0ENWBZs72X7T9U+NoHPM1ftUn/Mfw7dRDXrqWA6M1wBg6z6fGE32aeA==" crossorigin="anonymous"></script>
499 500
500<script src="https://unpkg.com/masonry-layout@4/dist/masonry.pkgd.min.js"></script>
500 501
501<script src="https://unpkg.com/imagesloaded@5/imagesloaded.pkgd.min.js"></script>
501 502
502<script defer src="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script>
502 503
503<script defer src="https://cdn.jsdelivr.net/npm/[email protected]/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous"></script>
503 504
504<script> 505 document.addEventListener("DOMContentLoaded", function() { 506 renderMathInElement(document.body, { 507 delimiters: [ 508 {left: '$$', right: '$$', display: true}, 509 {left: '$', right: '$', display: false} 510 ], 511 throwOnError : false 512 }); 513 }); 514 </script>
514 515
515<script src="/assets/js/common.js"></script>
515 516
516<script src="/assets/js/bubble_visual_hash.js"></script>
516 517
517<script src="/assets/js/semantic_scholar_citation_count.js"></script>
517 518</body> 519</html>
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.