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DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease.

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1 match between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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  1. [1] § Methods › Development of PlaqueNet › Model architecture and implementation detail ↔ pipeline/02_inference/configs/inference_config.py, lines 29–88 · score 0.72 · cross entropy loss, Swin transformer, backbone, heads, weighted, model

Paper

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The authors' code

Python · 167 lines · 5.3 KB · CC-BY-NC-ND-4.0 · 1 match

  1. default_scope = 'mmdet'
  2. backend_args = None
  4. # Test pipeline for inference
  5. test_pipeline = [
  6. dict(type='LoadImageFromFile', backend_args=None),
  7. dict(type='Resize', scale=(1333, 800), keep_ratio=True),
  8. dict(
  9. type='PackDetInputs',
  10. meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor'))
  11. ]
  13. # Test dataloader (required by DetInferencer)
  14. test_dataloader = dict(
  15. batch_size=1,
  16. num_workers=2,
  17. persistent_workers=True,
  18. drop_last=False,
  19. sampler=dict(type='DefaultSampler', shuffle=False),
  20. dataset=dict(
  21. type='CocoDataset',
  22. data_root='',
  23. ann_file='',
  24. data_prefix=dict(img=''),
  25. metainfo=dict(classes=('cored', 'diffuse', 'fibrillar')),
  26. test_mode=True,
  27. pipeline=test_pipeline))
  29. # Visualizer configuration
  30. vis_backends = [dict(type='LocalVisBackend')]
  31. visualizer = dict(
  32. type='DetLocalVisualizer',
  33. vis_backends=vis_backends,
  34. name='visualizer',
  35. alpha=0.3, # Mask transparency (lower = more transparent, default 0.8)
  36. line_width=2) # Contour line width (default 3)
  38. model = dict(
  39. type='MaskRCNN',
  40. data_preprocessor=dict(
  41. type='DetDataPreprocessor',
  42. mean=[123.675, 116.28, 103.53],
  43. std=[58.395, 57.12, 57.375],
  44. bgr_to_rgb=True,
  45. pad_mask=True,
  46. pad_size_divisor=32),
  47. backbone=dict(
  48. type='SwinTransformer',
  49. embed_dims=96,
  50. depths=[2, 2, 18, 2],
  51. num_heads=[3, 6, 12, 24],
  52. window_size=7,
  53. mlp_ratio=4,
  54. qkv_bias=True,
  55. qk_scale=None,
  56. drop_rate=0.0,
  57. attn_drop_rate=0.0,
  58. drop_path_rate=0.2,
  59. patch_norm=True,
  60. out_indices=(0, 1, 2, 3),
  61. with_cp=False,
  62. convert_weights=True),
  63. neck=dict(
  64. type='FPN',
  65. in_channels=[96, 192, 384, 768],
  66. out_channels=256,
  67. num_outs=5),
  68. rpn_head=dict(
  69. type='RPNHead',
  70. in_channels=256,
  71. feat_channels=256,
  72. anchor_generator=dict(
  73. type='AnchorGenerator',
  74. scales=[8],
  75. ratios=[0.5, 1.0, 2.0],
  76. strides=[4, 8, 16, 32, 64]),
  77. bbox_coder=dict(
  78. type='DeltaXYWHBBoxCoder',
  79. target_means=[0.0, 0.0, 0.0, 0.0],
  80. target_stds=[1.0, 1.0, 1.0, 1.0]),
  81. loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
  82. loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
  83. roi_head=dict(
  84. type='StandardRoIHead',
  85. bbox_roi_extractor=dict(
  86. type='SingleRoIExtractor',
  87. roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
  88. out_channels=256,
  89. featmap_strides=[4, 8, 16, 32]),
  90. bbox_head=dict(
  91. type='Shared2FCBBoxHead',
  92. in_channels=256,
  93. fc_out_channels=1024,
  94. roi_feat_size=7,
  95. num_classes=4,
  96. bbox_coder=dict(
  97. type='DeltaXYWHBBoxCoder',
  98. target_means=[0.0, 0.0, 0.0, 0.0],
  99. target_stds=[0.1, 0.1, 0.2, 0.2]),
  100. reg_class_agnostic=False,
  101. loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
  102. loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
  103. mask_roi_extractor=dict(
  104. type='SingleRoIExtractor',
  105. roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
  106. out_channels=256,
  107. featmap_strides=[4, 8, 16, 32]),
  108. mask_head=dict(
  109. type='FCNMaskHead',
  110. num_convs=4,
  111. in_channels=256,
  112. conv_out_channels=256,
  113. num_classes=4,
  114. loss_mask=dict(type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
  115. # Train config (required for model init, not used in inference)
  116. train_cfg=dict(
  117. rpn=dict(
  118. assigner=dict(
  119. type='MaxIoUAssigner',
  120. pos_iou_thr=0.7,
  121. neg_iou_thr=0.3,
  122. min_pos_iou=0.3,
  123. match_low_quality=True,
  124. ignore_iof_thr=-1),
  125. sampler=dict(
  126. type='RandomSampler',
  127. num=256,
  128. pos_fraction=0.5,
  129. neg_pos_ub=-1,
  130. add_gt_as_proposals=False),
  131. allowed_border=-1,
  132. pos_weight=-1,
  133. debug=False),
  134. rpn_proposal=dict(
  135. nms_pre=2000,
  136. max_per_img=1000,
  137. nms=dict(type='nms', iou_threshold=0.7),
  138. min_bbox_size=0),
  139. rcnn=dict(
  140. assigner=dict(
  141. type='MaxIoUAssigner',
  142. pos_iou_thr=0.5,
  143. neg_iou_thr=0.5,
  144. min_pos_iou=0.5,
  145. match_low_quality=True,
  146. ignore_iof_thr=-1),
  147. sampler=dict(
  148. type='RandomSampler',
  149. num=512,
  150. pos_fraction=0.25,
  151. neg_pos_ub=-1,
  152. add_gt_as_proposals=True),
  153. mask_size=28,
  154. pos_weight=-1,
  155. debug=False)),
  156. # Inference configuration
  157. test_cfg=dict(
  158. rpn=dict(
  159. nms_pre=1000,
  160. max_per_img=1000,
  161. nms=dict(type='nms', iou_threshold=0.85),
  162. min_bbox_size=100),
  163. rcnn=dict(
  164. score_thr=0.6,
  165. nms=dict(type='nms', iou_threshold=0.85),
  166. max_per_img=100,
  167. mask_thr_binary=0.85)))

inference_config.py at commit 02bcfc5, under CC-BY-NC-ND-4.0 · at the source

Overview

Authors: Hiu Yi Wong1,2,3, Cheng Jin4, Sze Long Yuen1,2, Xin Yang1,2, Sunveer Singh Gill1,2, Jiahui Xu1,2, Kuo Pin Eric Lai1,2, Wing-Yu Fu1,2, Weina Gao5, Xi Wang6, Jiani Wang5, Han Cao1,2, Ge Lv1,2, Kin Ying Mok1,2,7,8, Ruijun Tian9, Amy K Y Fu1,2,10,11, Hao Chen1,4,12,13, Nancy Y Ip1,2,10,11,3
ORCID iDs: Cheng Jin, Sunveer Singh Gill, Han Cao, Ge Lv, Ruijun Tian, Amy K Y Fu, Hao Chen, Nancy Y Ip
13 affiliations
  1. Division of Life Science, State Key Laboratory of Nervous System Disorders, Daniel and Mayce Yu Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China
  2. InnoHK Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong Special Administrative Region, China
  3. Present Address: School of Medicine, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China
  4. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China
  5. Bayomics Biotechnology Hong Kong Limited, Hong Kong Special Administrative Region, China
  6. Shenzhen BayOmics Biotechnology Co., Ltd, Shenzhen, China
  7. Department of Neurodegenerative Disease, University College London Institute of Neurology, London, UK
  8. School of Medicine, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China
  9. State Key Laboratory of Medical Proteomics and Shenzhen Key Laboratory of Functional Proteomics, Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science and Guangming Advanced Research Institute, Southern University of Science and Technology, Shenzhen, 518055 China
  10. Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development; Shenzhen-Hong Kong Institute of Brain Science, HKUST Shenzhen Research Institute, Shenzhen, Guangdong China
  11. SIAT–HKUST Joint Laboratory for Brain Science, Shenzhen, Guangdong China
  12. Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China
  13. HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China
Institutions: Hong Kong University of Science and Technology (Hong Kong SAR China); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); Southern University of Science and Technology (China); HKUST Shenzhen Research Institute (China); Shenzhen Institutes of Advanced Technology (China); Shenzhen-Hong Kong Institute of Brain Science (China)
Journal: EMBO molecular medicine, volume 18, issue 9, pages 3495-3516
Dates: received 6 March 2026; accepted 6 July 2026; published online 21 July 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44321-026-00488-4 · PMID 42481826 · PMCID PMC13562684 · OpenAlex W7169889331
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging
Keywords: Computational Biology, Neuroscience
MeSH: Alzheimer Disease*, Amyloid beta-Peptides*, Plaque, Amyloid*, Animals, Brain, Deep Learning, Humans, Laser Capture Microdissection, Microglia, Proteome, Proteomics (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Research Grants Council of Hong Kong (the General Research Fund) (HKUST16104624 and HKUST16102824, T13-605/18W); the Innovation and Technology Fund for State Key Laboratory (ITCPD/17-9); SIAT-HKUST Joint Laboratory for Brain Science (Joint laboratory Scheme) (JLFS/M-604/24); the National Natural Science Foundation of China (No. 62202403); InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government; the Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders (2023B1212120004); Areas of Excellence Scheme of the University Grants Committee (AoE/M-604/16)
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

Histological analysis is essential for understanding disease pathology and the microenvironment, particularly in Alzheimer’s disease (AD), characterized by beta-amyloid (Aβ) plaques that exist as diffuse, fibrillar, and core species, with distinct toxicity levels. However, accurate classification of Aβ plaque types in postmortem brain tissues and profiling of surrounding cells present significant challenges. To address these challenges, we developed “DeepPlaque”, an integrated system featuring “PlaqueNet”, a deep learning model for automated classification of Aβ plaque species from diverse imaging platforms. DeepPlaque includes automated workflows for cellular phenotyping and proteomic profiling through targeted laser microdissection. PlaqueNet achieves expert-level accuracy (AUC > 90%) in classifying the 3 major Aβ plaque species, supporting consistent and large-scale annotation. By integrating spatial cellular phenotyping with laser microdissection, DeepPlaque enables high-throughput proteomic analysis of Aβ plaque niches, revealing that microglia are more abundant around core and fibrillar Aβ plaques, with increased expression of apolipoprotein E and amyloid precursor protein in core Aβ plaques. This customizable platform enhances the molecular and cellular characterization of Aβ plaque-associated environments, providing critical insights into AD pathology.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

ChengJin-git/DeepPlaque

License: CC-BY-NC-ND-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 02bcfc5e534b50298e49b93c994f34e56356eef4, 18 December 2025
Languages: Python (2), Shell (1)
Size: 51 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, environment (environment.yml, pipeline/02_inference/requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: OpenCV (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/ChengJin-git/DeepPlaque

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Data links

Data availability

The mass spectrometry dataset has been deposited into the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al, 2022) partner repository with the dataset identifier PXD071421 (http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD071421). The deposited dataset is available at https://ebi.ac.uk/pride/archive/projects/PXD071421. All DeepPlaque model source code is available under a CC BY-NC-ND 4.0 license at https://www.github.com/ChengJin-git/DeepPlaque.

The source data of this paper are collected in the following database record: biostudies:S-SCDT-10_1038-S44321-026-00488-4 (https://www.ebi.ac.uk/biostudies/sourcedata/studies/S-SCDT-10_1038-S44321-026-00488-4).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 2 keywords, 11 MeSH terms, 7 funders, 64 references.

Cite

This paper

Wong, H. Y., Jin, C., Yuen, S. L., Yang, X., Gill, S. S., Xu, J., Lai, K. P. E., Fu, W.-Y., Gao, W., Wang, X., Wang, J., Cao, H., Lv, G., Mok, K. Y., Tian, R., Fu, A. K. Y., Chen, H., & Ip, N. Y. (2026). DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease. EMBO molecular medicine, 18(9), 3495-3516. https://doi.org/10.1038/s44321-026-00488-4

BibTeX

@article{wong2026deepplaque,
author = {Wong, Hiu Yi and Jin, Cheng and Yuen, Sze Long and Yang, Xin and Gill, Sunveer Singh and Xu, Jiahui and Lai, Kuo Pin Eric and Fu, Wing-Yu and Gao, Weina and Wang, Xi and Wang, Jiani and Cao, Han and Lv, Ge and Mok, Kin Ying and Tian, Ruijun and Fu, Amy K Y and Chen, Hao and Ip, Nancy Y},
title = {{DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease}},
journal = {EMBO molecular medicine},
year = {2026},
month = jul,
volume = {18},
number = {9},
pages = {3495--3516},
publisher = {Nature Publishing Group},
issn = {1757-4676},
doi = {10.1038/s44321-026-00488-4},
url = {https://doi.org/10.1038/s44321-026-00488-4},
pmid = {42481826},
pmcid = {PMC13562684}
}

RIS

TY - JOUR
AU - Wong, Hiu Yi
AU - Jin, Cheng
AU - Yuen, Sze Long
AU - Yang, Xin
AU - Gill, Sunveer Singh
AU - Xu, Jiahui
AU - Lai, Kuo Pin Eric
AU - Fu, Wing-Yu
AU - Gao, Weina
AU - Wang, Xi
AU - Wang, Jiani
AU - Cao, Han
AU - Lv, Ge
AU - Mok, Kin Ying
AU - Tian, Ruijun
AU - Fu, Amy K Y
AU - Chen, Hao
AU - Ip, Nancy Y
TI - DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease
T2 - EMBO molecular medicine
J2 - EMBO Mol Med
PY - 2026
DA - 2026/07/21
VL - 18
IS - 9
SP - 3495
EP - 3516
SN - 1757-4676
PB - Nature Publishing Group
DO - 10.1038/s44321-026-00488-4
UR - https://doi.org/10.1038/s44321-026-00488-4
LA - en
ER -

CSL-JSON

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