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46    <div class="container-lg">
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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
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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                    
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