Fetching the paper…
Reading the bibliography…
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications.
Applying watershed algorithms to the segmentation of clustered nuclei
N. Malpica, C. O. de Solórzano, J.J. Vaquero, A. Santos, I. Vallcorba, and J. M. García-Sagredo, et al · 1998
Earlier work this paper cites.
PanNuke dataset extension, insights and baselines
J. Gamper, N. A. Koohbanani, K. Benes, S. Graham, M. Jahanifar, and S. A. Khurram, et al · 2003
Earlier work this paper cites.
Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy
X. Yang, H. Li, and X. Zhou · 2006
Earlier work this paper cites.
Segmentation of clustered nuclei with shape markers and marking function
J. Cheng and J. C. Rajapakse · 2008
Earlier work this paper cites.
Extraction of informative cell features by segmentation of densely clustered tissue images
S. Kothari, Q. Chaudry, and M.D. Wang · 2009
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, and T. Unterthiner, et al · 2010
Earlier work this paper cites.
An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery
S. Ali and A. Madabhushi · 2012
Earlier work this paper cites.
Detection and segmentation of cell nuclei in virtual microscopy images: A minimum-model approach
S. Wienert, D. Heim, K. Saeger, A. Stenzinger, M. Beil, and P. Hufnagl, et al · 2012
Earlier work this paper cites.
Automatic nuclei segmentation in h&e stained breast cancer histopathology images
M. Veta, P. J. van Diest, R. Kornegoor, A. Huisman, M. A. Viergever, and J. P. W. Pluim · 2013
Earlier work this paper cites.
Automatic segmentation for cell images based on bottleneck detection and ellipse fitting
M. Liao, Y. Q. Zhao, X. H. Li, P. S. Dai, X. W. Xu, and J. K. Zhang, et al · 2015
Earlier work this paper cites.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Clinical significance of tumor-infiltrating lymphocytes in breast cancer
S. E. Stanton and M. L. Disis · 2016
Earlier work this paper cites.
Accurate cervical cell segmentation from overlapping clumps in pap smear images
Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, and S. Chen, et al · 2016
Earlier work this paper cites.
Dcan: Deep contour-aware networks for accurate gland segmentation
H. Chen, X. Qi, L. Yu, and P. Heng · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images
K. Sirinukunwattana, S. E. A. Raza, Y. W. Tsang, D. Snead, I. A. Cree, and N. M Rajpoot · 2016
Earlier work this paper cites.
Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features
G. Corredor, J. Whitney, V. Arias, A. Madabhushi, and E. Romero · 2017
Earlier work this paper cites.
Mask r-cnn
K. He, G. Gkioxari, P. Dollar, and R. Girshick · 2017
Earlier work this paper cites.
Fast r-cnn
R. Girshick · 2017
Earlier work this paper cites.
Feature pyramid networks for object detection
T. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, and A. N. Gomez, et al · 2017
Earlier work this paper cites.
QuPath: Open source software for digital pathology image analysis
P. Bankhead, M. B. Loughrey, J. A. Fernández, Y. Dombrowski, D. G. McArt, and P. D. Dunne, et al · 2017
Earlier work this paper cites.
A dataset and a technique for generalized nuclear segmentation for computational pathology
N. Kumar, R. Verma, S. Sharma, S. Bhargava, A. Vahadane, and A. Sethi · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
Earlier work this paper cites.
Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer
K. Sirinukunwattana, D. Snead, D. Epstein, Z. Aftab, I. Mujeeb, and Y. W. Tsang, et al · 2018
Cited alongside, same era.
Multi-pass fast watershed for accurate segmentation of overlapping cervical cells
A. Tareef, Y. Song, H. Huang, D. Feng, M. Chen, and Y. Wang, et al · 2018
Cited alongside, same era.
Micro-net: A unified model for segmentation of various objects in microscopy images
S. E. Raza, L. Cheung, M. Shaban, S. Graham, D. Epstein, and S. Pelengaris, et al · 2018
Cited alongside, same era.
Segmentation of nuclei in histopathology images by deep regression of the distance map
P. Naylor, M. Laé, F. Reyal, and T. Walter · 2018
Cited alongside, same era.
Cell detection with star-convex polygons
Uwe Schmidt, Martin Weigert, Coleman Broaddus, and Gene Myers · 2018
Cited alongside, same era.
UNETR: Transformers for 3D medical image segmentation
A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, and B. Landman, et al · 2021
Later among the works it cites.
Image segmentation using deep learning: A survey
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos · 2021
Later among the works it cites.
U-net and its variants for medical image segmentation: A review of theory and applications
N. Siddique, S. Paheding, C. P. Elkin, and V. Devabhaktuni · 2021
Later among the works it cites.
Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, and P. Bojanowski, et al · 2021
Later among the works it cites.
Do vision transformers see like convolutional neural networks?
M. Raghu, T. Unterthiner, S. Kornblith, C. Zhang, and A. Dosovitskiy · 2021
Later among the works it cites.
Vit-yolo:transformer-based yolo for object detection
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Y. Lin, P. Goyal, R. Girshick, Ross K. He, and P. Dollár · 2018
Cited alongside, same era.
Inflammation and cancer: Triggers, mechanisms, and consequences
F. R. Greten and S. I. Grivennikov · 2019
Cited alongside, same era.
Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y. W. Tsang, and J. T. Kwak, et al · 2019
Cited alongside, same era.
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
G. Campanella, M. G. Hanna, L. Geneslaw, A. Miraflor, V. Werneck, and K. Silva, et al · 2019
Cited alongside, same era.
Wie funktioniert radiomics?
J. M. Murray, G. Kaissis, R. Braren, and J. Kleesiek · 2019
Cited alongside, same era.
A guide to deep learning in healthcare
A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, and K. Chou, et al · 2019
Cited alongside, same era.
Nuclear instance segmentation using a proposal-free spatially aware deep learning framework
N. A. Koohbanani, M Jahanifar, A. Gooya, and N. Rajpoot · 2019
Cited alongside, same era.
Z. Zhang, X. Lu, G. Cao, Y. Yang, L. Jiao, and F. Liu · 2021
Later among the works it cites.
Transformers make strong encoders for medical image segmentation
L. Y. Chen and Q. Yu · 2021
Later among the works it cites.
Medical image segmentation using squeeze-and-expansion transformers
S. Li, X. Sui, X. Luo, X. Xu, Y. Liu, and R. Goh · 2021
Later among the works it cites.
Segformer: Simple and efficient design for semantic segmentation with transformers
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo · 2021
Later among the works it cites.
Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, and Y Wang, et al · 2021
Later among the works it cites.
Exploring simple siamese representation learning
X. Chen and K. He · 2021
Later among the works it cites.
On the opportunities and risks of foundation models
R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, and S. von Arx, et al · 2021
Later among the works it cites.
The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the global burden of disease study 2019
K. B. Tran, J. J. Lang, K. Compton, R. Xu, A. R. Acheson, and H. J. Henrikson, et al · 2022
Later among the works it cites.
TSFD-Net: Tissue specific feature distillation network for nuclei segmentation and classification
T. Ilyas, Z. I. Mannan, A. Khan, S. Azam, H. Kim, and F. De Boer · 2022
Later among the works it cites.
One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification
S. Graham, Q. D. Vu, M. Jahanifar, S. E. A. Raza, F. Minhas, and D. Snead, et al · 2022
Later among the works it cites.
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
R. J. Chen, C. Chen, Y. Li, T. Y. Chen, A. D. Trister, and R. G Krishnan, et al · 2022
Later among the works it cites.
Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)
B. S. Kelly, C. Judge, S. M. Bollard, S. M. Clifford, G. M. Healy, and A. Aziz, et al · 2022
Later among the works it cites.
Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist
M. Weigert and U. Schmidt · 2022
Later among the works it cites.
Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu · 2022
Later among the works it cites.
okunator/cellseg_models.pytorch: v0.1.23, 2022
Okunator · 2022
Later among the works it cites.
Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology
O. Ester, F. Hörst, C. Seibold, J. Keyl, S. Ting, and N. Vasileiadis, et al · 2023
Closest in time.
Histology-based prediction of therapy response to neoadjuvant chemotherapy for esophageal and esophagogastric junction adenocarcinomas using deep learning
F. Hörst, S. Ting, S. T. Liffers, K. L. Pomykala, K. Steiger, and M. Albertsmeier, et al · 2023
Closest in time.
Segment anything
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, and L. Gustafson, et al · 2023
Closest in time.
CPP-Net: Context-aware polygon proposal network for nucleus segmentation
S. Chen, C. Ding, M. Liu, J. Cheng, and D. Tao · 2023
Closest in time.