

@ARTICLE{Clayton1991,
	title={Pathologic correlates of survival in 378 lymph node-negative
	infiltrating ductal breast carcinomas. Mitotic count is the best
	single predictor},
	author   = {Clayton, F},
	abstract = {Pathologic features of 378 breast carcinomas without axillary
	lymph node metastases were correlated with long-term prognosis.
	Mitotic count, the feature best predicting prognosis, was most
	useful at lower mitotic count levels than usually studied. Cases
	with less than 4.5 mitotic figures per ten high-power fields had
	a 20-year tumor-related survival of 73\% versus 48\% for those
	with higher mitotic counts. Low and high mitotic count groups had
	significantly different prognoses for T1c, T2, and T3 tumors.
	Multivariate analysis showed that the best combination of
	features predicting tumor-related death were high mitotic count
	(relative risk, x 2.8), large tumor size (relative risk, x 1.7),
	lymphatic invasion (relative risk, x 1.7), and skin or subjacent
	muscle or chest wall invasion (relative risk, x 2.5). Nuclear
	grade, Bloom-Richardson grade, modified Scarff-Bloom-Richardson
	grade, Fisher's grade, and necrosis correlated well with survival
	but had no significant additional predictive value when adjusted
	for the mitotic count.},
	journal  = {Cancer},
	volume   =  {68},
	number   =  {6},
	pages    = {1309--1317},
	month    =  {sep},
	year     =  {1991},
	address  = {United States},
	language = {en}
}

@ARTICLE{Elston1991,
	title    = {Pathological prognostic factors in breast cancer. I. The value of
	histological grade in breast cancer: experience from a large
	study with long-term follow-up},
	author   = {Elston, C W and Ellis, I O},
	abstract = {Morphological assessment of the degree of differentiation has
	been shown in numerous studies to provide useful prognostic
	information in breast cancer, but until recently histological
	grading has not been accepted as a routine procedure, mainly
	because of perceived problems with reproducibility and
	consistency. In the Nottingham/Tenovus Primary Breast Cancer
	Study the most commonly used method, described by Bloom \&
	Richardson, has been modified in order to make the criteria more
	objective. The revised technique involves semiquantitative
	evaluation of three morphological features--the percentage of
	tubule formation, the degree of nuclear pleomorphism and an
	accurate mitotic count using a defined field area. A numerical
	scoring system is used and the overall grade is derived from a
	summation of individual scores for the three variables: three
	grades of differentiation are used. Since 1973, over 2200
	patients with primary operable breast cancer have been entered
	into a study of multiple prognostic factors. Histological grade,
	assessed in 1831 patients, shows a very strong correlation with
	prognosis; patients with grade I tumours have a significantly
	better survival than those with grade II and III tumours (P less
	than 0.0001). These results demonstrate that this method for
	histological grading provides important prognostic information
	and, if the grading protocol is followed consistently,
	reproducible results can be obtained. Histological grade forms
	part of the multifactorial Nottingham prognostic index, together
	with tumour size and lymph node stage, which is used to stratify
	individual patients for appropriate therapy.},
	journal  = {Histopathology},
	volume   =  {19},
	number   =  {5},
	pages    = {403--410},
	month    =  {nov},
	year     =  {1991},
	address  = {England},
	language = {en}
}


@article{Cui_2019,
	title={A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images},
	volume={57},
	ISSN={1741-0444},
	url={http://dx.doi.org/10.1007/s11517-019-02008-8},
	DOI={10.1007/s11517-019-02008-8},
	number={9},
	journal={Medical \& Biological Engineering \& Computing},
	publisher={Springer Science and Business Media LLC},
	author={Cui, Yuxin and Zhang, Guiying and Liu, Zhonghao and Xiong, Zheng and Hu, Jianjun},
	year={2019},
	month=jul, pages={2027–2043} }

@article{IMTIAZ2023107378,
	title = {BAWGNet: Boundary aware wavelet guided network for the nuclei segmentation in histopathology images},
	journal = {Computers in Biology and Medicine},
	volume = {165},
	pages = {107378},
	year = {2023},
	issn = {0010-4825},
	doi = {https://doi.org/10.1016/j.compbiomed.2023.107378},
	url = {https://www.sciencedirect.com/science/article/pii/S0010482523008430},
	author = {Tamjid Imtiaz and Shaikh Anowarul Fattah and Sun-Yuan Kung},
	keywords = {Nucleus segmentation, Discrete wavelet transform, Deep neural network, Attention mechanism, Boundary aware unit},
	abstract = {Precise cell nucleus segmentation is very critical in many biologically related analyses and disease diagnoses. However, the variability in nuclei structure, color, and modalities of histopathology images make the automatic computer-aided nuclei segmentation task very difficult. Traditional encoder–decoder based deep learning schemes mainly utilize the spatial domain information that may limit the performance of recognizing small nuclei regions in subsequent downsampling operations. In this paper, a boundary aware wavelet guided network (BAWGNet) is proposed by incorporating a boundary aware unit along with an attention mechanism based on a wavelet domain guidance in each stage of the encoder–decoder output. Here the high-frequency 2 Dimensional discrete wavelet transform (2D-DWT) coefficients are utilized in the attention mechanism to guide the spatial information obtained from the encoder–decoder output stages to leverage the nuclei segmentation task. On the other hand, the boundary aware unit (BAU) captures the nuclei’s boundary information, ensuring accurate prediction of the nuclei pixels in the edge region. Furthermore, the preprocessing steps used in our methodology confirm the data’s uniformity by converting it to similar color statistics. Extensive experimentations conducted on three benchmark histopathology datasets (DSB, MoNuSeg and TNBC) exhibit the outstanding segmentation performance of the proposed method (with dice scores 90.82%, 85.74%, and 78.57%, respectively). Implementation of the proposed architecture is available at https://github.com/tamjidimtiaz/BAWGNet.}
}

@misc{Sampias_Rolls, 
	title={H\&E staining overview: A guide to best practices},
	url={https://www.leicabiosystems.com/en-kr/knowledge-pathway/he-staining-overview-a-guide-to-best-practices/}, 
	journal={An Intro to H\&E Staining: Protocol, Best Practices, Steps \& More}, 
	publisher={Leica Biosystems}, 
	author={Sampias, Cindy and Rolls, Geoffrey}} 

@ARTICLE{Davey2006,
	title    = {Effect of study design and quality on unsatisfactory rates,
	cytology classifications, and accuracy in liquid-based versus
	conventional cervical cytology: a systematic review},
	author   = {Davey, Elizabeth and Barratt, Alexandra and Irwig, Les and Chan,
	Siew F and Macaskill, Petra and Mannes, Patricia and Saville, A
	Marion},
	abstract = {BACKGROUND: Liquid-based cytology is reported to increase the
	sensitivity of cervical cytology and the proportion of slides
	that are satisfactory for assessment, in comparison with
	conventional cytology. Although some countries have changed to
	liquid-based cytology for cervical screening, controversy
	remains. We reviewed the published work to assess the performance
	of liquid-based cytology relative to conventional cytology in
	primary studies assessed to be of low, medium, or high
	methodological quality. METHODS: 56 primary studies were reviewed
	and assessed with strict methodological criteria. Liquid-based
	cytology and conventional cytology were compared in terms of the
	percentage of slides classified as unsatisfactory, the percentage
	of slides classified in each cytology category, and the accuracy
	of detection of high-grade disease. Data were examined for
	studies overall and in strata to examine the effect of study
	quality on results. FINDINGS: The median difference in the
	percentage of unsatisfactory slides between liquid-based cytology
	and conventional cytology was 0.17\%. Only one small study was a
	randomised controlled trial. The classification of high-grade
	squamous epithelial lesion varied according to study quality
	(p=0.04), with conventional cytology classifying more slides in
	this category than did liquid-based cytology in high-quality
	studies (n=3) only. In medium-quality (n=30) and high-quality
	studies, liquid-based cytology classified more slides as atypical
	squamous cells of unknown significance than did conventional
	cytology when compared with low-quality studies (n=17; p=0.05).
	Only four studies provided sufficient verified data to allow
	estimation of sensitivity and specificity and comparison of test
	accuracy. INTERPRETATION: We saw no evidence that liquid-based
	cytology reduced the proportion of unsatisfactory slides, or
	detected more high-grade lesions in high-quality studies, than
	conventional cytology. This review does not lend support to
	claims of better performance by liquid-based cytology. Large
	randomised controlled trials are needed.},
	journal  = {Lancet},
	volume   =  {367},
	number   =  {9505},
	pages    = {122--132},
	month    =  {jan},
	year     =  {2006},
	address  = {England},
	language = {en}
}

@ARTICLE{Irshad2014,
	title    = {Methods for nuclei detection, segmentation, and classification in
	digital histopathology: a review-current status and future
	potential},
	author   = {Irshad, Humayun and Veillard, Antoine and Roux, Ludovic and
	Racoceanu, Daniel},
	abstract = {Digital pathology represents one of the major evolutions in
	modern medicine. Pathological examinations constitute the gold
	standard in many medical protocols, and also play a critical and
	legal role in the diagnosis process. In the conventional cancer
	diagnosis, pathologists analyze biopsies to make diagnostic and
	prognostic assessments, mainly based on the cell morphology and
	architecture distribution. Recently, computerized methods have
	been rapidly evolving in the area of digital pathology, with
	growing applications related to nuclei detection, segmentation,
	and classification. In cancer research, these approaches have
	played, and will continue to play a key (often bottleneck) role
	in minimizing human intervention, consolidating pertinent second
	opinions, and providing traceable clinical information.
	Pathological studies have been conducted for numerous cancer
	detection and grading applications, including brain, breast,
	cervix, lung, and prostate cancer grading. Our study presents,
	discusses, and extracts the major trends from an exhaustive
	overview of various nuclei detection, segmentation, feature
	computation, and classification techniques used in histopathology
	imagery, specifically in hematoxylin-eosin and
	immunohistochemical staining protocols. This study also enables
	us to measure the challenges that remain, in order to reach
	robust analysis of whole slide images, essential high content
	imaging with diagnostic biomarkers and prognosis support in
	digital pathology.},
	journal  = {IEEE Rev Biomed Eng},
	volume   =  {7},
	pages    = {97--114},
	year     =  {2014},
	address  = {United States},
	language = {en}
}

@ARTICLE{Chow2012,
	title    = {The nuclear envelope environment and its cancer connections},
	author   = {Chow, Kin-Hoe and Factor, Rachel E and Ullman, Katharine S},
	abstract = {Nuclear morphology is often altered in cancer. Irregularity in
	nuclear contours is a feature used by pathologists in diagnostic
	cytology.The nuclear envelope provides a specialized
	microenvironment within the nucleus. It is also the site where a
	series of protein--protein interactions take place to connect the
	cytoskeleton to the interior of the nucleus.Constituents of the
	nuclear envelope --- lamins, nuclear pore complexes and lipid
	membranes --- have key roles in several processes that affect
	tumour cell biology and response to therapy.Changes in the
	abundance or the function of components of the nuclear envelope
	in tumour cells can cause the occurrence of dysmorphic nuclei and
	can deregulate cell migration, intracellular signalling, DNA
	repair, cell division and gene expression.Tracking the abundance
	of molecular components of the nuclear envelope environment and
	enhanced methods to visualize changes at the nuclear envelope
	could potentially be used for the prognostic assessment of cancer
	patients.},
	journal  = {Nature Reviews Cancer},
	volume   =  {12},
	number   =  {3},
	pages    = {196--209},
	month    =  {mar},
	year     =  {2012}
}

@ARTICLE{ZHANG2020,
	title = {DENSE-INception U-net for medical image segmentation},
	journal = {Computer Methods and Programs in Biomedicine},
	volume = {192},
	pages = {105395},
	year = {2020},
	issn = {0169-2607},
	doi = {https://doi.org/10.1016/j.cmpb.2020.105395},
	url = {https://www.sciencedirect.com/science/article/pii/S0169260719307904},
	author = {Ziang Zhang and Chengdong Wu and Sonya Coleman and Dermot Kerr},
	keywords = {Deep learning, Medical image segmentation, U-net, GoogLeNet, DenseNet},
	abstract = {Background and objective
	Convolutional neural networks (CNNs) play an important role in the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture is one of the most famous fully convolutional network architectures for medical semantic segmentation tasks. Recent work shows that the U-net network can be substantially deeper thus resulting in improved performance on segmentation tasks. Though adding more layers directly into network is a popular way to make a network deeper, it may lead to gradient vanishing or redundant computation during training.
	Methods
	A novel CNN architecture is proposed that integrates the Inception-Res module and densely connecting convolutional module into the U-net architecture. The proposed network model consists of the following parts: firstly, the Inception-Res block is designed to increase the width of the network by replacing the standard convolutional layers; secondly, the Dense-Inception block is designed to extract features and make the network more deep without additional parameters; thirdly, the down-sampling block is adopted to reduce the size of feature maps to accelerate learning and the up-sampling block is used to resize the feature maps.
	Results
	The proposed model is tested on images of blood vessel segmentations from retina images, the lung segmentation of CT Data from the benchmark Kaggle datasets and the MRI scan brain tumor segmentation datasets from MICCAI BraTS 2017. The experimental results show that the proposed method can provide better performance on these two tasks compared with the state-of-the-art algorithms. The results reach an average Dice score of 0.9857 in the lung segmentation. For the blood vessel segmentation, the results reach an average Dice score of 0.9582. For the brain tumor segmentation, the results reach an average Dice score of 0.9867.
	Conclusions
	The experiments highlighted that combining the inception module with dense connections in the U-Net architecture is a promising approach for semantic medical image segmentation.}
}

@INPROCEEDINGS{Liuetal,
	author={Liu, Zequan and Wang, Suhong and Chen, Zidong and Xiao, Zhifeng},
	booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}, 
	title={UCITransNet: A Deep Neural Model with FC-CRFs and Object-Level Data Augmentation for Cell Instance Segmentation}, 
	year={2023},
	volume={},
	number={},
	pages={1-5},
	doi={10.1109/ISBI53787.2023.10230712}}
	
@misc{ronneberger2015unet,
	title={U-Net: Convolutional Networks for Biomedical Image Segmentation}, 
	author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
	year={2015},
	eprint={1505.04597},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@misc{li2023fusionunet,
	title={FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentation}, 
	author={Zongyi Li and Hongbing Lyu and Jun Wang},
	year={2023},
	eprint={2310.10951},
	archivePrefix={arXiv},
	primaryClass={eess.IV}
}

@article{Siddique_2021,
	title={U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications},
	volume={9},
	ISSN={2169-3536},
	url={http://dx.doi.org/10.1109/ACCESS.2021.3086020},
	DOI={10.1109/access.2021.3086020},
	journal={IEEE Access},
	publisher={Institute of Electrical and Electronics Engineers (IEEE)},
	author={Siddique, Nahian and Paheding, Sidike and Elkin, Colin P. and Devabhaktuni, Vijay},
	year={2021},
	pages={82031–82057} }
	
@misc{zhou2018unet,
	title={UNet++: A Nested U-Net Architecture for Medical Image Segmentation}, 
	author={Zongwei Zhou and Md Mahfuzur Rahman Siddiquee and Nima Tajbakhsh and Jianming Liang},
	year={2018},
	eprint={1807.10165},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}
@misc{oktay2018attention,
	title={Attention U-Net: Learning Where to Look for the Pancreas}, 
	author={Ozan Oktay and Jo Schlemper and Loic Le Folgoc and Matthew Lee and Mattias Heinrich and Kazunari Misawa and Kensaku Mori and Steven McDonagh and Nils Y Hammerla and Bernhard Kainz and Ben Glocker and Daniel Rueckert},
	year={2018},
	eprint={1804.03999},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@article{Qin_2020,
	title={U2-Net: Going deeper with nested U-structure for salient object detection},
	volume={106},
	ISSN={0031-3203},
	url={http://dx.doi.org/10.1016/j.patcog.2020.107404},
	DOI={10.1016/j.patcog.2020.107404},
	journal={Pattern Recognition},
	publisher={Elsevier BV},
	author={Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Dehghan, Masood and Zaiane, Osmar R. and Jagersand, Martin},
	year={2020},
	month=oct, pages={107404} }

@misc{chen2021transunet,
	title={TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation}, 
	author={Jieneng Chen and Yongyi Lu and Qihang Yu and Xiangde Luo and Ehsan Adeli and Yan Wang and Le Lu and Alan L. Yuille and Yuyin Zhou},
	year={2021},
	eprint={2102.04306},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@ARTICLE{Qiao2023,
	title    = {MBUTransNet: multi-branch U-shaped network fusion transformer
	architecture for medical image segmentation},
	author   = {Qiao, Junbo and Wang, Xing and Chen, Ji and Liu, Mingtao},
	abstract = {Recently, transformers have been adopted to computer vision
	applications and achieve great success in image segmentation.
	However by simply applying transformers to medical segmentation
	task it is hard to achieve much higher accuracy than by
	traditional U-shaped network structures, which are based on CNNs
	and has been extensively researched. On the other hand, CNN
	structure pays more attention to local information and ignores
	global information, which is very important for the medical image
	segmentation dataset with cell scattered background. This
	motivates us to explore the feasibility of using U-shape
	effective fusion transformer network architectures for medical
	image segmentation tasks.},
	journal  = {International Journal of Computer Assisted Radiology and Surgery},
	volume   =  {18},
	number   =  {10},
	pages    = {1895--1902},
	month    =  {oct},
	year     =  {2023}
}

@article{li_semi_supervised_2023,
	title = {Semi-supervised nuclei segmentation based on multi-edge features fusion attention network},
	volume = {18},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0286161},
	doi = {10.1371/journal.pone.0286161},
	abstract = {The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches.},
	language = {en},
	number = {5},
	urldate = {2024-01-12},
	journal = {PLOS ONE},
	author = {Li, Huachang and Zhong, Jing and Lin, Liyan and Chen, Yanping and Shi, Peng},
	editor = {Ahmad, Mubashir},
	month = may,
	year = {2023},
	pages = {e0286161}
}

@misc{ma2023segment,
	title={Segment Anything in Medical Images}, 
	author={Jun Ma and Yuting He and Feifei Li and Lin Han and Chenyu You and Bo Wang},
	year={2023},
	eprint={2304.12306},
	archivePrefix={arXiv},
	primaryClass={eess.IV}
}

@article{kirillov2023segany,
	title={Segment Anything},
	author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
	journal={arXiv:2304.02643},
	year={2023}
}

@misc{shaharabany2023autosam,
	title={AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder}, 
	author={Tal Shaharabany and Aviad Dahan and Raja Giryes and Lior Wolf},
	year={2023},
	eprint={2306.06370},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@misc{wei2023imedsam,
	title={I-MedSAM: Implicit Medical Image Segmentation with Segment Anything}, 
	author={Xiaobao Wei and Jiajun Cao and Yizhu Jin and Ming Lu and Guangyu Wang and Shanghang Zhang},
	year={2023},
	eprint={2311.17081},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@ARTICLE{monuseg_ds,
	author={Kumar, Neeraj and Verma, Ruchika and Sharma, Sanuj and Bhargava, Surabhi and Vahadane, Abhishek and Sethi, Amit},
	journal={IEEE Transactions on Medical Imaging}, 
	title={A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology}, 
	year={2017},
	volume={36},
	number={7},
	pages={1550-1560},
	doi={10.1109/TMI.2017.2677499}}

@misc{huang2020unet,
	title={UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation}, 
	author={Huimin Huang and Lanfen Lin and Ruofeng Tong and Hongjie Hu and Qiaowei Zhang and Yutaro Iwamoto and Xianhua Han and Yen-Wei Chen and Jian Wu},
	year={2020},
	eprint={2004.08790},
	archivePrefix={arXiv},
	primaryClass={eess.IV}
}

@misc{dauphin2017language,
	title={Language Modeling with Gated Convolutional Networks}, 
	author={Yann N. Dauphin and Angela Fan and Michael Auli and David Grangier},
	year={2017},
	eprint={1612.08083},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
}

@misc{zhang2019making,
	title={Making Convolutional Networks Shift-Invariant Again}, 
	author={Richard Zhang},
	year={2019},
	eprint={1904.11486},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@misc{ghiasi2018dropblock,
	title={DropBlock: A regularization method for convolutional networks}, 
	author={Golnaz Ghiasi and Tsung-Yi Lin and Quoc V. Le},
	year={2018},
	eprint={1810.12890},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@misc{wu2019fast,
	title={Fast End-to-End Trainable Guided Filter}, 
	author={Huikai Wu and Shuai Zheng and Junge Zhang and Kaiqi Huang},
	year={2019},
	eprint={1803.05619},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@article{ZHAN2023104112,
	title = {CFNet: A medical image segmentation method using the multi-view attention mechanism and adaptive fusion strategy},
	journal = {Biomedical Signal Processing and Control},
	volume = {79},
	pages = {104112},
	year = {2023},
	issn = {1746-8094},
	doi = {https://doi.org/10.1016/j.bspc.2022.104112},
	url = {https://www.sciencedirect.com/science/article/pii/S1746809422005699},
	author = {Bangcheng Zhan and Enmin Song and Hong Liu and Zhenyu Gong and Guangzhi Ma and Chih-Cheng Hung}
}

@InProceedings{Wyatt_2022_CVPR,
	author    = {Wyatt, Julian and Leach, Adam and Schmon, Sebastian M. and Willcocks, Chris G.},
	title     = {AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise},
	booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
	month     = {June},
	year      = {2022},
	pages     = {650-656}
}

@Article{Bae2018,
	author={Bae, Hyun-Jin
	and Kim, Chang-Wook
	and Kim, Namju
	and Park, BeomHee
	and Kim, Namkug
	and Seo, Joon Beom
	and Lee, Sang Min},
	title={A Perlin Noise-Based Augmentation Strategy for Deep Learning with Small Data Samples of HRCT Images},
	journal={Scientific Reports},
	year={2018},
	month={Dec},
	day={06},
	volume={8},
}

@article{KASCENAS2023102963,
	title = {The role of noise in denoising models for anomaly detection in medical images},
	journal = {Medical Image Analysis},
	volume = {90},
	pages = {102963},
	year = {2023},
	issn = {1361-8415},
	doi = {https://doi.org/10.1016/j.media.2023.102963},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841523002232},
	author = {Antanas Kascenas and Pedro Sanchez and Patrick Schrempf and Chaoyang Wang and William Clackett and Shadia S. Mikhael and Jeremy P. Voisey and Keith Goatman and Alexander Weir and Nicolas Pugeault and Sotirios A. Tsaftaris and Alison Q. O’Neil}
}

@article{10.1145/325165.325247,
	author = {Perlin, Ken},
	title = {An image synthesizer},
	year = {1985},
	issue_date = {Jul. 1985},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	volume = {19},
	number = {3},
	issn = {0097-8930},
	url = {https://doi.org/10.1145/325165.325247},
	doi = {10.1145/325165.325247},
	abstract = {We introduce the concept of a Pixel Stream Editor. This forms the basis for an interactive synthesizer for designing highly realistic Computer Generated Imagery. The designer works in an interactive Very High Level programming environment which provides a very fast concept/implement/view iteration cycle.Naturalistic visual complexity is built up by composition of non-linear functions, as opposed to the more conventional texture mapping or growth model algorithms. Powerful primitives are included for creating controlled stochastic effects. We introduce the concept of "solid texture" to the field of CGI.We have used this system to create very convincing representations of clouds, fire, water, stars, marble, wood, rock, soap films and crystal. The algorithms created with this paradigm are generally extremely fast, highly realistic, and asynchronously parallelizable at the pixel level.},
	journal = {SIGGRAPH Comput. Graph.},
	month = {jul},
	pages = {287–296},
	numpages = {10},
	keywords = {waves, turbulence, stochastic modelling, space function, solid texture, pixel stream editor, interactive, functional composition, fire, algorithm development}
}

@inproceedings{perlinNoise,
	author = {Perlin, Ken},
	title = {An image synthesizer},
	year = {1985},
	isbn = {0897911660},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
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@misc{deng2023segment,
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@misc{vu2018methods,
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	primaryClass={cs.CV}
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@misc{graham2019hovernet,
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	eprint={1812.06499},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
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@ARTICLE{reinhardetal,
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@INPROCEEDINGS{patiletal,
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@misc{agarap2019deep,
	title={Deep Learning using Rectified Linear Units (ReLU)}, 
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	archivePrefix={arXiv},
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@inbook{mahbodetal,
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	month = {07},
	pages = {75-82},
	title = {A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H\&E-Stained Tissues},
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@article{LIU2022593,
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	pages = {593-600},
	year = {2022},
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	author = {Shaohua Liu and Yisu Wang and Jingkai Sun and Tianlu Mao}
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@misc{lin2018focal,
	title={Focal Loss for Dense Object Detection}, 
	author={Tsung-Yi Lin and Priya Goyal and Ross Girshick and Kaiming He and Piotr Dollár},
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	eprint={1708.02002},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@misc{wu2019fast,
	title={Fast End-to-End Trainable Guided Filter}, 
	author={Huikai Wu and Shuai Zheng and Junge Zhang and Kaiqi Huang},
	year={2019},
	eprint={1803.05619},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@INPROCEEDINGS{macenko,
	author={Macenko, Marc and Niethammer, Marc and Marron, J. S. and Borland, David and Woosley, John T. and Xiaojun Guan and Schmitt, Charles and Thomas, Nancy E.},
	booktitle={2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro}, 
	title={A method for normalizing histology slides for quantitative analysis}, 
	year={2009},
	volume={},
	number={},
	pages={1107-1110},
	keywords={Fluorescence;Microscopy;Information retrieval;Deconvolution;Image retrieval;Diffraction;Detectors;Biological cells;Maximum likelihood estimation;Testing;Biomedical microscopy;Biomedical image processing;Biomedical signal detection;Melanoma;Pathology},
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@ARTICLE{elu_net,
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	title={ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation}, 
	year={2022},
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	doi={10.1109/ACCESS.2022.3163711}}

@misc{isensee2018nnunet,
	title={nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation}, 
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	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@misc{wu2019fast,
	title={Fast End-to-End Trainable Guided Filter}, 
	author={Huikai Wu and Shuai Zheng and Junge Zhang and Kaiqi Huang},
	year={2019},
	eprint={1803.05619},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@misc{isensee2018nnunet,
	title={nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation}, 
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	year={2018},
	eprint={1809.10486},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@article{Eelbode_2020,
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	volume={39},
	ISSN={1558-254X},
	url={http://dx.doi.org/10.1109/TMI.2020.3002417},
	DOI={10.1109/tmi.2020.3002417},
	number={11},
	journal={IEEE Transactions on Medical Imaging},
	publisher={Institute of Electrical and Electronics Engineers (IEEE)},
	author={Eelbode, Tom and Bertels, Jeroen and Berman, Maxim and Vandermeulen, Dirk and Maes, Frederik and Bisschops, Raf and Blaschko, Matthew B.},
	year={2020},
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@misc{rezatofighi2019generalized,
	title={Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression}, 
	author={Hamid Rezatofighi and Nathan Tsoi and JunYoung Gwak and Amir Sadeghian and Ian Reid and Silvio Savarese},
	year={2019},
	eprint={1902.09630},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
}

@article{Fu2024TSCANetTB,
	title={TSCA-Net: Transformer based spatial-channel attention segmentation network for medical images},
	author={Yinghua Fu and Junfeng Liu and Jun Shi},
	journal={Computers in Biology and Medicine},
	year={2024},
	volume={170},
	pages={107938}
}


