In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some more potential methods are proposed, and further prospects are made.
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Stony Brook University（石溪大学）；Shaukat Khanum Cancer Hospital（肖卡特·卡努姆癌症医院）；Australian National University（澳大利亚国立大学）
Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology
Mohamed Bin Zayed University of Artificial Intelligence（Mohamed Bin Zayed人工智能大学）；Khalifa University（卡勒比大学）；Linköping University（林霍尔姆大学）；Australian National University（澳大利亚国立大学）
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology
University Of Utah（犹他大学）；Scientific Computing and Imaging Institute（科学计算与成像研究所）；Department of Pathology, University of Utah（犹他大学病理学系）；Kahlert School of Computing, University Of Utah（犹他大学卡尔勒特计算学院）
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）；School of Electrical Engineering, Shahrood Branch, Islamic Azad University（伊斯兰阿扎德大学沙赫鲁德分校电气工程学院）；Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究分支医学科学与技术部门）
Stony Brook University（石溪大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Stony Brook University Hospital（石溪大学医院）
Histopathology images are crucial to the study of complex diseases such as cancer. The histologic characteristics of nuclei play a key role in disease diagnosis, prognosis and analysis. In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. Our CAE is the first unsupervised detection network for computer vision applications. T…
Advancement in digital pathology and artificial intelligence has enabled deep learning-based computer vision techniques for automated disease diagnosis and prognosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel-sized, making them infeasible to be used directly for training deep neural networks. Hence, for modeling, a two-stage approach is adopted: Patch representations are extracted first, followed by the aggregation for WSI prediction. These approaches require detailed pixel-level annotations for training the patch encoder. However, obtaining thes…
Nuclei segmentation is a fundamental task that is critical for various computational pathology applications including nuclei morphology analysis, cell type classification, and cancer grading. Conventional vision-based methods for nuclei segmentation struggle in challenging cases and deep learning approaches have proven to be more robust and generalizable. However, CNNs require large amounts of labeled histopathology data. Moreover, conventional CNN-based approaches lack structured prediction capabilities which are required to distinguish overlapping and clumped nuclei. Here, we present an appr…
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
Low-resource finetuning of foundation models beats state-of-the-art in histopathology
School of Computation and Information Technology, Technical University of Munich（慕尼黑工业大学计算与信息技术学院）；Helmholtz Zentrum München - German Research Center for Environmental Health（慕尼黑亥姆霍兹中心——德国环境健康研究中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Top Data Science Ltd.（顶级数据科学有限公司）；University of Helsinki（赫尔辛基大学）；Skåne University Hospital（斯科讷大学医院）；Helsinki University Hospital（赫尔辛基大学医院）
Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopath…
Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowle…
Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning
PLRI Medical Informatics Institute（PLRI医学信息学研究所）；CAIMed Reserch Center（CAIMed研究中心）；Hannover Medical School（汉诺威医学院）；Computer Science Institute（计算机科学研究所）；Leibniz University Hannover（莱比锡大学汉诺威分校）；Sanford Burnham Prebys Medical Discovery Institute（桑福医疗发现研究所）；Rady Children’s Institute for Genomic Medicine（拉迪儿童基因医学研究所）；University of California San Diego（加州大学圣地亚哥分校）
Stony Brook Medicine（石溪大学医学中心）；Stony Brook University（石溪大学）；Cold Spring Harbor Laboratory（冷泉港实验室）
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability classification. The state-of-the-art for each task often employs base architectures that have been pretrained for image classification on ImageNet. The standard approach to develop classifiers in histopathology tends to focus narrowly on optimizing models for a single task, not considering the aspects of modeling innovatio…
We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH on the other hand contains dense diagnostic and morphological descriptions for a range of stains, tissue types and pathologies. Using intrinsic dimensionality estimation, we show that ARCH is the only CP dataset to (ARCH-)rival its computer vision analog MS-COCO Captions. We conjecture that an encoder pre-trained on dense image captions learns transferable representations for most CP tasks. We support the conjecture w…
Due to the recent advancements in machine vision, digital pathology has gained significant attention. Histopathology images are distinctly rich in visual information. The tissue glass slide images are utilized for disease diagnosis. Researchers study many methods to process histopathology images and facilitate fast and reliable diagnosis; therefore, the availability of high-quality slides becomes paramount. The quality of the images can be negatively affected when the glass slides are ink-marked by pathologists to delineate regions of interest. As an example, in one of the largest public histo…
ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology
Research Center for Medical Image Analysis（医学图像分析研究中心）；Artificial Intelligence, Department of Medicine, Faculty of Medicine（人工智能，医学系，医学学院）；Dentistry, Danube Private University Department of Pathophysiology（牙科，丹ube私立大学病理学系）；Allergy Research, Center of Pathophysiology, Infectiology（过敏研究，病理学中心，感染学）；Immunology, Medical University of Vienna Comprehensive Center for AI in Medicine (CAIM), Medical University of Vienna（免疫学，维也纳医学大学人工智能医学中心（CAIM），维也纳医学大学）
Burdur Mehmet Akif Ersoy University（布尔杜尔穆罕默德·阿基夫·埃尔索伊大学）；Süleyman Demirel University（苏莱曼·德米雷尔大学）
The aim of this study is to propose an alternative and hybrid solution method for diagnosing the disease from histopathology images taken from animals with paratuberculosis and intact intestine. In detail, the hybrid method is based on using both image processing and deep learning for better results. Reliable disease detection from histo-pathology images is known as an open problem in medical image processing and alternative solutions need to be developed. In this context, 520 histopathology images were collected in a joint study with Burdur Mehmet Akif Ersoy University, Faculty of Veterinary …
Faculty of Informatics and Data Science, University of Regensburg, Germany（信息学与数据科学学院，雷根萨大学）；Indian Institute of Information Technology Bhopal, India（比哈尔信息学院）
ConStruct: Structural Distillation of Foundation Models for Prototype-Based Weakly Supervised Histopathology Segmentation
Ho Chi Minh City University of Technology（胡志明市技术大学）；University of Technology Sydney（悉尼技术大学）；University of Houston（休斯顿大学）；University of Information Technology（信息科技大学）；College of Medical Science and Technology, Taipei Medical University（台北医学院医学科技学院）；Department of Computer Science, Emory University（埃默里大学计算机科学系）；Montefiore Medical Center, Albert Einstein College of Medicine（蒙特福伊医疗中心，阿尔伯特·爱因斯坦医学院）；School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）
Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping
University of Tsukuba（筑波大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Nagoya University（名古屋大学）；Kurume University（久留米大学）；Tokyo Institute of Technology（东京工业大学）
New York University（纽约大学）；IIIT Hyderabad（印度海得拉巴国际信息技术研究所）
Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body. Lupus Nephritis (LN) refers to the inflammation of kidney tissues resulting in renal failure due to these attacks. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) has released a classification system based on various patterns observed during renal injury in SLE. Traditional methods require meticulous pathological assessment of the renal biopsy and are time-consuming. Recently, computational techniques have helped to allevi…
Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology
University of Tübingen（蒂宾根大学）；University of Lucerne（卢塞恩大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Institut du Cancer de l’Ouest（西部癌症研究所）；Oncode Institute（Oncode研究所）
Retrieval-Guided Generation for Safer Histopathology Image Captioning
Kimia Lab, Department of Artificial Intelligence \& Informatics, Mayo Clinic, Rochester, MN, USA；Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA；Lane Department of Computer Science；Electrical Engineering, West Virginia University, Morgantown, WV, USA；Department of Computer Science；Engineering, Princeton University, Princeton, NJ, USA；Department of Computer Sciences, University of Wisconsin--Madison, Madison, WI, USA
Effortless Vision-Language Model Specialization in Histopathology without Annotation
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-厄林根-纽伦堡大学）；Ingolstadt University of Applied Sciences（因戈尔施塔特应用科学大学）；Flensburg University of Applied Sciences（弗拉森堡应用科学大学）；Julius-Maximilians-Universität Würzburg（朱利叶斯-马克斯-魏扎克大学）
