Histological Image Analysis Research Articles

Characterization of Inductive Moderate Hyperthermia Effects on Intratumor Sarcoma-45 Heterogeneity Using Magnetic Resonance, Ultrasound and Histology Image Analysis

Characterization of Inductive Moderate Hyperthermia Effects on Intratumor Sarcoma-45 Heterogeneity Using Magnetic Resonance, Ultrasound and Histology Image Analysis

Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.

Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract extensive visual characteristics for correct categorization. To address this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of several deep-learning architectures and use aggregate knowledge of many models to improve classification performance, allowing for more accurate and efficient gastric cancer detection. To determine how well these proposed models performed, this study compared them with other works, all of which were based on the Gastric Histopathology Sub-Size Images Database, a publicly available dataset for gastric cancer. This research demonstrates that the ensemble models achieved a high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80-pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120-pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160-pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, highlighting the ensemble model's superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings show that ensemble models may successfully detect critical characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates.

Evaluation test and analysis of a microneedle and iontophoresis based medical device "CELLADEEP Patch" in skin improvement on ex vivo human-derived skin tissue models.

Microneedles are tiny needles, typically ranging from tens to hundreds of micrometers in length, used in various medical procedures and treatments. The tested medical device named "CELLADEEP Patch" a dissolvable microneedle therapy system (MTS), made of hyaluronic acid and collagen. And the iontophoresis technique is also applied in the system. The study aimed to evaluate the effectiveness of the "CELLADEEP Patch" in skin improvement. Ex vivo human-derived skin tissue models were used in this study and they were divided into three different groups, namely, the Untreated Group, the Negative Control Group, and the Test Group respectively. The Untreated Group received no treatment measures, the Negative Control Group was exposed to ultraviolet B radiation (UVB) irradiation, and the Test Group was exposed to UVB irradiation and treated with "CELLADEEP Patch". Skin moisture content, transdermal water loss, and skin elasticity were evaluated by three clinical devices. Additionally, histological staining and related mRNA expression levels were also analyzed. The results of skin moisture content, transdermal water loss, and skin elasticity evaluation consistently illustrated that the application of "CELLADEEP Patch" led to remarkable skin improvement. And the analysis of histological staining images also confirmed the effectiveness of the "CELLADEEP Patch", especially for increasing collagen density. Moreover, the upregulation of Collagen type 1 a (COL1A1) and hyaluronan synthase 3 mRNA expression and the decrease of Matrix metalloproteinase 1 (MMP-1) and Interleukin-1 beta (IL-1β) mRNA expression reflected its wrinkle improvement, moisturizing and anti-inflammation function. "CELLADEPP Patch", the MTS combined with the iontophoresis technique, exhibits its effectiveness in moisturizing, skin elasticity improvement, and anti-inflammatory function when applied to ex vivo human-derived skin tissue models in experiments. The study has contributed to the understanding of the "CELLADEPP Patch" and laid the foundation for subsequent animal experiments and clinical trials.

Review of computer-assisted diagnosis model to classify follicular lymphoma histology.

The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.

Towards Metric-Driven Difference Detection between Receptive and Nonreceptive Endometrial Samples Using Automatic Histology Image Analysis

This paper presents a technique that can potentially help to determine the receptivity stage of the endometrium from histology images by automatically measuring the stromal nuclear changes. The presented technique is composed of an image segmentation model and the statistical evolution of segmented areas in hematoxylin and eosin (HE)-stained histology images. Three different endometrium receptivity stages, namely pre-receptive, post-receptive, and receptive, were compared. An ensemble-based AI model was proposed for histology image segmentation, which is based on individual UNet++, UNet, and ResNet34-UNet segmentation models. The performance of the ensemble-based technique was assessed using the Dice score and intersection over unit (IoU) values. In comparison to alternative segmentation architectures that were applied singly, the current ensemble-based method obtained higher Dice score (0.95) and IoU (0.90) values. The statistical comparison highlighted a noticeable difference in the number of nuclei and the size of the stroma tissue. The proposed technique demonstrated the positive potential for practical implementation for automatic endometrial tissue analysis.

ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.

The development of next-generation tissue-engineered medical devices such as tissue-engineered vascular grafts (TEVGs) is a leading trend in translational medicine. Microscopic examination is an indispensable part of animal experimentation, and histopathological analysis of regenerated tissue is crucial for assessing the outcomes of implanted medical devices. However, the objective quantification of regenerated tissues can be challenging due to their unusual and complex architecture. To address these challenges, research and development of advanced ML-driven tools for performing adequate histological analysis appears to be an extremely promising direction. We compiled a dataset of 104 representative whole slide images (WSIs) of TEVGs which were collected after a 6-month implantation into the sheep carotid artery. The histological examination aimed to analyze the patterns of vascular tissue regeneration in TEVGs in situ. Having performed an automated slicing of these WSIs by the Entropy Masker algorithm, we filtered and then manually annotated 1,401 patches to identify 9 histological features: arteriole lumen, arteriole media, arteriole adventitia, venule lumen, venule wall, capillary lumen, capillary wall, immune cells, and nerve trunks. To segment and quantify these features, we rigorously tuned and evaluated the performance of six deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, and MA-Net). After rigorous hyperparameter optimization, all six deep learning models achieved mean Dice Similarity Coefficients (DSC) exceeding 0.823. Notably, FPN and PSPNet exhibited the fastest convergence rates. MA-Net stood out with the highest mean DSC of 0.875, demonstrating superior performance in arteriole segmentation. DeepLabV3 performed well in segmenting venous and capillary structures, while FPN exhibited proficiency in identifying immune cells and nerve trunks. An ensemble of these three models attained an average DSC of 0.889, surpassing their individual performances. This study showcases the potential of ML-driven segmentation in the analysis of histological images of tissue-engineered vascular grafts. Through the creation of a unique dataset and the optimization of deep neural network hyperparameters, we developed and validated an ensemble model, establishing an effective tool for detecting key histological features essential for understanding vascular tissue regeneration. These advances herald a significant improvement in ML-assisted workflows for tissue engineering research and development.

Effects of Sapindus mukorossi Seed Oil on Bone Healing Efficiency: An Animal Study.

Natural products have attracted great interest in the development of tissue engineering. Recent studies have demonstrated that unsaturated fatty acids found in natural plant seed oil may exhibit positive osteogenic effects; however, few in vivo studies have focused on the use of plant seed oil for bone regeneration. The aim of this study is to investigate the effects of seed oil found in Sapindus mukorossi (S. mukorossi) on the osteogenic differentiation of mesenchymal stem cells and bone growth in artificial bone defects in vivo. In this study, Wharton-jelly-derived mesenchymal stem cells (WJMSCs) were co-cultured with S. mukorossi seed oil. Cellular osteogenic capacity was assessed using Alizarin Red S staining. Real-time PCR was carried out to evaluate ALP and OCN gene expression. The potential of S. mukorossi seed oil to enhance bone growth was assessed using an animal model. Four 6 mm circular defects were prepared at the parietal bone of New Zealand white rabbits. The defects were filled with hydrogel and hydrogel-S. mukorossi seed oil, respectively. Quantitative analysis of micro-computed tomography (Micro-CT) and histological images was conducted to compare differences in osteogenesis between oil-treated and untreated samples. Although our results showed no significant differences in viability between WJMSCs treated with and without S. mukorossi seed oil, under osteogenic conditions, S. mukorossi seed oil facilitated an increase in mineralized nodule secretion and upregulated the expression of ALP and OCN genes in the cells (p < 0.05). In the animal study, both micro-CT and histological evaluations revealed that new bone formation in artificial bone defects treated with S. mukorossi seed oil were nearly doubled compared to control defects (p < 0.05) after 4 weeks of healing. Based on these findings, it is reasonable to suggest that S. mukorossi seed oil holds promise as a potential candidate for enhancing bone healing efficiency in bone tissue engineering.

Reproductive biology and nuptial coloration of Trachinus draco (Trachinidae) from Atlantic Spanish waters (SW Europe)

Studies focusing on reproductive biology play a key role in deciphering fish population dynamics. We studied the main features of the reproductive biology of the greater weever Trachinus draco based on gonad histology and digital image analysis techniques and the main hypothesis of the study is that males of T. draco exhibit a nuptial colouration. The biological samples of 311 individuals along the Atlantic coast of Spain, covering the twelve months of the year (from 2020 to 2022), in combination with the gonadosomatic index trend revealed a spawning season from May to August, consistent with similar studies in different geographical areas. Length-weight relationship presented differences by sex (females reach higher weight at the same length). The maturity ogive for females indicated that 50 % were mature at a length of 182 mm. Histological analysis confirmed asynchronous development of secondary growth oocytes, pointing to a batch spawning strategy. Digital image analysis of the nuptial coloration showed that the presence of a black spot above the pectoral fin in males was significantly correlated with the spawning capable phase, while blueish coloration was influenced by sex, size and maturity phase, with larger spawning males having the largest areas of blueish coloration. The results of this study represent a significant improvement of our understanding of the reproductive biology of T. draco.

Masked pre-training of transformers for histology image analysis

In digital pathology, whole-slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Vision transformer (ViT) models have recently emerged as a promising method for encoding large regions of WSIs while preserving spatial relationships among patches. However, due to the large number of model parameters and limited labeled data, applying transformer models to WSIs remains challenging. In this study, we propose a pretext task to train the transformer model in a self-supervised manner. Our model, MaskHIT, uses the transformer output to reconstruct masked patches, measured by contrastive loss. We pre-trained MaskHIT model using over 7000 WSIs from TCGA and extensively evaluated its performance in multiple experiments, covering survival prediction, cancer subtype classification, and grade prediction tasks. Our experiments demonstrate that the pre-training procedure enables context-aware understanding of WSIs, facilitates the learning of representative histological features based on patch positions and visual patterns, and is essential for the ViT model to achieve optimal results on WSI-level tasks. The pre-trained MaskHIT surpasses various multiple instance learning approaches by 3% and 2% on survival prediction and cancer subtype classification tasks, and also outperforms recent state-of-the-art transformer-based methods. Finally, a comparison between the attention maps generated by the MaskHIT model with pathologist's annotations indicates that the model can accurately identify clinically relevant histological structures on the whole slide for each task.

Revolutionizing prostate cancer diagnosis: Unleashing the potential of an optimized deep belief network for accurate Gleason grading in histological images

PC (Prostate Cancer) is the second highest cause of death due to cancer in men globally. Proper detection and treatment are critical for halting or controlling the growth and spread of cancer cells within the human organism. However, evaluating these sorts of images is difficult and time-consuming, requiring histopathological image recognition as the most reliable method for treating PC because of its distinct visual characteristics. Risk evaluation and treatment planning rely heavily on histological image-based Gleason grading of prostate tumors. This work introduces an innovative approach to histological image analysis for prostate cancer diagnosis and Gleason grading. The Elephant Herding Optimization-based Hyper-parameter Convolutional Deep Belief Network (CDBN-EHO) is presented alongside a grading network head-optimized deep belief network technique for multi-task prediction. Leveraging an effective Bayesian inference method, fully linked Conditional Random Field (CRF) techniques are utilized for segmentation, with pairwise boundary capacities determined by a linear mixture of Gaussian kernels. The multi-task approach aims to enhance performance by incorporating contextual information, leading to breakthrough results in the identification of epithelial cells and the grading of Gleason scores. The objective of this study is to demonstrate the effectiveness of the optimized deep belief network technique in improving diagnostic accuracy and efficiency for prostate cancer diagnosis and Gleason grading in histological images.

A novel approach for color-balanced reference image selection for breast histology image normalization

Histology image analysis plays a crucial role in pathological research and diagnosis. The effectiveness of Computer-Aided Detection procedures largely depends on the normalization of histology images to ensure precise diagnoses. On the other hand, the success of this normalization process is tied to the choice of an appropriate reference image, which acts as a standard for aligning and modifying the color and intensity of other images in the dataset. However, the lack of a universally reliable method for selecting an appropriate reference image remains a substantial obstacle in histology image analysis. In this direction, the study introduces an automated procedure for selecting a reference image that is designed to accurately represent the entire spectrum of the dataset or its specific class under investigation. The impact of the proposed method on the morphological and structural quality of normalized histology images is investigated using three established normalization techniques: Reinhard, Macenko, and Vahadane. Moreover, the study also utilized two strategies: the global reference image strategy, where a single reference image is chosen for the entire dataset, and the class-specific reference image strategy, which considers the unique characteristics of each class. The results demonstrate that the proposed automatic reference image selection procedure consistently outperforms random selection in both strategies, yielding higher metric scores (SSIM, QSSIM, and PCC). Furthermore, the class-specific reference image approach significantly improves the image quality compared to random selection. The proposed method enhances alignment and accuracy in histology image normalization by leveraging class-specific reference images, facilitating more reliable analysis in histopathology research and clinical applications.

Orthotopic transplantation of the bioengineered lung using a mouse-scale perfusion-based bioreactor and human primary endothelial cells

Whole lung engineering and the transplantation of its products is an ambitious goal and ultimately a viable solution for alleviating the donor-shortage crisis for lung transplants. There are several limitations currently impeding progress in the field with a major obstacle being efficient revascularization of decellularized scaffolds, which requires an extremely large number of cells when using larger pre-clinical animal models. Here, we developed a simple but effective experimental pulmonary bioengineering platform by utilizing the lung as a scaffold. Revascularization of pulmonary vasculature using human umbilical cord vein endothelial cells was feasible using a novel in-house developed perfusion-based bioreactor. The endothelial lumens formed in the peripheral alveolar area were confirmed using a transmission electron microscope. The quality of engineered lung vasculature was evaluated using box-counting analysis of histological images. The engineered mouse lungs were successfully transplanted into the orthotopic thoracic cavity. The engineered vasculature in the lung scaffold showed blood perfusion after transplantation without significant hemorrhage. The mouse-based lung bioengineering system can be utilized as an efficient ex-vivo screening platform for lung tissue engineering.

Test-Time Adaptation via Style and Structure Guidance for Histological Image Registration

Image registration plays a crucial role in histological image analysis, encompassing tasks like multi-modality fusion and disease grading. Traditional registration methods optimize objective functions for each image pair, yielding reliable accuracy but demanding heavy inference burdens. Recently, learning-based registration methods utilize networks to learn the optimization process during training and apply a one-step forward process during testing. While these methods offer promising registration performance with reduced inference time, they remain sensitive to appearance variances and local structure changes commonly encountered in histological image registration scenarios. In this paper, for the first time, we propose a novel test-time adaptation method for histological image registration, aiming to improve the generalization ability of learning-based methods. Specifically, we design two operations, style guidance and shape guidance, for the test-time adaptation process. The former leverages style representations encoded by feature statistics to address the issue of appearance variances, while the latter incorporates shape representations encoded by HOG features to improve registration accuracy in regions with structural changes. Furthermore, we consider the continuity of the model during the test-time adaptation process. Different from the previous methods initialized by a given trained model, we introduce a smoothing strategy to leverage historical models for better generalization. We conduct experiments with several representative learning-based backbones on the public histological dataset, demonstrating the superior registration performance of our test-time adaptation method.

ERNet : Enhanced ResNet for classification of breast histopathological images

Inspite of expeditious approaches in field of breast cancer, histopathological analysis is considered as gold standard in diagnosis of cancer. Researchers are working tremendously to automate the detection and analysis of breast histology images, which confess in improving the accuracy and also induce the mimisation of processing time. Deep learning models are providing greater contribution in solving several image classification tasks. In this paper we propose a model to classify breast histological images, which is redesigned from existing ResNet architecture that minimises model parameters and increase computational efficiency. This approach uses enhanced ResNet connection instead of identity shortcut connection used in ResNet architecture. We apply our proposed method on BreakHis dataset and achieve an accuracy around 95.92 %. The numerical results show that our proposed approach outperforms the previous methods with respect to sensitivity and accuracy.

Predicting Breast Cancer Risk from Histopathology Images using Hybrid Deep Learning Classifier

Millions of people per year pass away from breast cancer (BC), which is a fatal illness. To deal with this issue more effectively, diagnoses can be made more scalable and less prone to mistakes by developing automated malignant BC detection systems applied on patient's imaging. Not less significant is the fact that this type of research can be expanded to include additional cancer types, having a greater influence on helping to save lives. Recent BC recognition results demonstrate that Convolution Neural Networks (CNN) can outperform manually crafted feature descriptors in terms of recognition rates. However, this higher recognition rate comes at the cost of a more complex system to develop, one that requires more time for training and specialized knowledge to fine-tune the CNN's architecture. The comprehensive understanding of the structural and cellular attributes of tissue samples that histopathological image analysis imparts is regarded as a crucial component in the rapid detection and diagnosis of breast cancer. By means of this analysis, pathologists are capable of discerning aberrant cellular proliferation, ascertaining the existence of malignant tumours, and evaluating the degree of cancer advancement. The utilization of sophisticated imaging technologies and computational algorithms in histopathological image analysis improves the precision and effectiveness of diagnosis, allowing medical practitioners to expeditiously commence suitable therapeutic strategies. In conclusion, this instrument makes a substantial contribution towards enhancing the efficacy of breast cancer management in the field of clinical practice. However, because to its ineffectiveness, the identification of breast cancer is still an unresolved problem in medical image analysis. By combining deep learning and machine learning techniques, we were able to create a categorization system built on histology images that significantly improved the accuracy of early breast cancer diagnosis while relieving doctors of some of their effort. Multilayer Perceptron and LightGBM classifiers are used in this study's analysis of histology images of breast cancer (BC). Histopathology images are the gold standard when it comes to making a diagnosis of breast cancer. Here, a dataset of 3104 photos is used to train our model, and the image is subsequently successfully identified. This strategy also yields impressive analysis and findings. We test the suggested strategy using histology images from the Breast Cancer dataset, and we achieve a high classification accuracy of 98.28%. The results of the experiments demonstrate that our strategy can perform pretty favourably and exceed cutting-edge approaches.In the near future, using deep learning to predict breast cancer may show to be quite successful.

Adipose Tissue Analysis Toolkit (ATAT) for automated analysis of adipocyte size and extracellular matrix in white adipose tissue.

The pathological expansion of white adipose tissue (WAT) in obesity involves adipocyte hypertrophy accompanied by expansion of the collagen-rich pericellular extracellular matrix (ECM) and development of crown-like structures (CLS). Traditionally, WAT morphology is assessed through immunohistochemical analysis of WAT sections. However, manual analysis of large histological sections is time-consuming, and the available digital tools for analyzing adipocyte size and pericellular ECM are limited. To address this gap, the authors developed the Adipose Tissue Analysis Toolkit (ATAT), an ImageJ plugin facilitating analysis of adipocyte size, WAT ECM, and CLS. ATAT utilizes local and image-level differentials in pixel intensity to independently threshold image background, distinguishing adipocyte-free tissue without user input. It accurately captures adipocytes in histological sections stained with common dyes and automates the analysis of adipocyte cross-sectional area, total-field, and localized region-of-interest ECM. ATAT allows fully automated batch analysis of histological images using default or user-defined adipocyte detection parameters. ATAT provides several advantages over existing WAT image analysis tools, enabling high-throughput analyses of adipocyte-specific parameters and facilitating the assessment of ECM changes associated with WAT remodeling due to weight changes and other pathophysiological alterations that affect WAT function.

MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke : AComparative Study of MT-Retrieved Clots and Clot Analogs.

Assessing clot composition on prethrombectomy computed tomography (CT) imaging may help in stroke treatment planning. In this study we seek to use microCT imaging of fabricated blood clots to understand the relationship between CT radiographic signals and the biological makeup. Clots (n = 10) retrieved by mechanical thrombectomy (MT) were collected, and 6clot analogs of varying RBC composition were made. We performed paired microCT and histological image analysis of all 16clots using aScanCo microCT 100 (4.9 µm resolution) and standard H&E staining (imaged at 40×). From these data types, first order statistic (FOS) radiomics were computed from microCT, and percent composition of RBCs (%RBC) was computed from histology. Polynomial and linear regression (LR) were used to build statistical models based on retrieved thrombus microCT and %RBC that were evaluated for their ability to predict the %RBC of clot analogs from mean HU. Correlation analyses of microCT FOS with composition were completed for both retrieved clots and analogs. The LR model fits relating MT-retrieved clot %RBC with mean (R2 = 0.625, p = 0.006) and standard deviation (R2 = 0.564, p < 0.05) in HUs on microCT were significant. Similarly, LR models relating analog histological %RBC to analog protocol %RBC (R2 = 0.915, p = 0.003) and mean HUs on microCT (R2 = 0.872, p = 0.007) were also significant. When the LR model built using MT-retrieved clots was used to predict analog %RBC from mean HUs, significant correlation was observed between predictions and actual histological %RBC (R2 = 0.852, p = 0.009). For retrieved clots, significant correlations were observed for energy and total energy with %RBC and %FP (|R| > 0.7, q < 0.01). Analogs further demonstrated significant correlation between FOS energy, total energy, variance and %WBC (|R| > 0.9, q < 0.01). MicroCT can be used to build models that predict AIS clot composition from routine CT parameters and help us to better understand radiomic signatures associated with clot composition and first pass outcomes. In future work, such observations can be used to better infer clot composition and inform thrombectomy prognostics from pretreatment CTs.

Federated Texture Classification: Implementing Colorectal Histology Image Analysis using Federated Learning

Federated Texture Classification: Implementing Colorectal Histology Image Analysis using Federated Learning

Unsupervised Non-rigid Histological Image Registration Guided by Keypoint Correspondences Based on Learnable Deep Features with Iterative Training.

Histological image registration is a fundamental task in histological image analysis. It is challenging because of substantial appearance differences due to multiple staining. Keypoint correspondences, i.e., matched keypoint pairs, have been introduced to guide unsupervised deep learning (DL) based registration methods to handle such a registration task. This paper proposes an iterative keypoint correspondence-guided (IKCG) unsupervised network for non-rigid histological image registration. Fixed deep features and learnable deep features are introduced as keypoint descriptors to automatically establish keypoint correspondences, the distance between which is used as a loss function to train the registration network. Fixed deep features extracted from DL networks that are pre-trained on natural image datasets are more discriminative than handcrafted ones, benefiting from the deep and hierarchical nature of DL networks. The intermediate layer outputs of the registration networks trained on histological image datasets are extracted as learnable deep features, which reveal unique information for histological images. An iterative training strategy is adopted to train the registration network and optimize learnable deep features jointly. Benefiting from the excellent matching ability of learnable deep features optimized with the iterative training strategy, the proposed method can solve the local non-rigid large displacement problem, an inevitable problem usually caused by misoperation, such as tears in producing tissue slices. The proposed method is evaluated on the Automatic Non-rigid Histology Image Registration (ANHIR) website and AutomatiC Registration Of Breast cAncer Tissue (ACROBAT) website. It ranked 1st on both websites as of August 6th, 2024.

Advancing Oncology Diagnostics: AI-Enabled Early Detection of Lung Cancer Through Hybrid Histological Image Analysis

Advancing Oncology Diagnostics: AI-Enabled Early Detection of Lung Cancer Through Hybrid Histological Image Analysis