Method For Detection Of Tumors Research Articles

Mouse Models of HER2+ Human Breast Cancer: A Literature Review

Introduction: HER2+ breast cancer (BC) makes up 15-20% of BC cases, where HER2 overexpression drives BC progression via proliferative signals. Targeted therapies inhibit HER2's activity but face challenges like resistance and metastasis. Thus, HER2+ BC is a developing research area where many preclinical studies are being conducted, and mice are often used due to their genetic and physiological similarities to humans. This study aims to review and compare existing mouse models to determine the best model of HER2+ BC. Methods: A literature search on PubMed in February 2024 using search terms including HER2, neu, neuNT, MMTV, and mammary tumors identified 16 primary research articles that used Neu-overexpressing mice. The papers were categorized under five models: MMTV-Neu, MMTV-NeuNT, FloxNeo-NeuNT, MMTV-NIC, and MMTV-HER2. Results: Among examined studies, Andrechek et al.'s FloxNeo-NeuNT model had the longest average tumor onset at 447 days, while Kuang et al.'s MMTV-NIC had the shortest at 126 days. Ursini-Segal et al.'s MMTV-NIC mice showed 100% mammary tumor incidence. Most models resulted in mammary adenocarcinomas, with lung metastases commonly observed, especially in papers that used MMTV-NIC. Discussion: Human HER2+ BC is commonly adenocarcinoma and often metastasizes to lungs, bones, liver, and brain. The mouse models were also found to be adenocarcinomas, but they primarily showed lung metastasis, possibly due to insufficient tumor detection methods for brain and bone metastasis. Neu copy numbers in Andrechek et al.’s FloxNeo-NeuNT model also align with findings on HER2 copy number amplification in human HER2+ BC. Conclusion: We have found that MMTV-NIC is the optimal mouse model for HER2+ BC research due to its short latency, 100% tumor incidence, and high rate of lung metastasis.

Empowering Brain Tumor Diagnosis through Explainable Deep Learning

Brain tumors are among the most lethal diseases, and early detection is crucial for improving patient outcomes. Currently, magnetic resonance imaging (MRI) is the most effective method for early brain tumor detection due to its superior imaging quality for soft tissues. However, manual analysis of brain MRI scans is prone to errors, largely influenced by the radiologists’ experience and fatigue. To address these challenges, computer-aided diagnosis (CAD) systems are more significant. These advanced computer vision techniques such as deep learning provide accurate predictions based on medical images, enhancing diagnostic precision and reliability. This paper presents a novel CAD framework for multi-class brain tumor classification. The framework employs six pre-trained deep learning models as the base and incorporates comprehensive data preprocessing and augmentation strategies to enhance computational efficiency. To address issues related to transparency and interpretability in deep learning models, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize the decision-making processes involved in tumor classification from MRI scans. Additionally, a user-friendly Brain Tumor Detection System has been developed using Streamlit, demonstrating its practical applicability in real-world settings and providing a valuable tool for clinicians. All simulation results are derived from a public benchmark dataset, showing that the proposed framework achieves state-of-the-art performance, with accuracy approaching 99% in ResNet-50, Xception, and InceptionV3 models.

Needle Tip Tracking through Photoluminescence for Minimally Invasive Surgery.

Minimally invasive surgery continues to prioritize patient safety by improving imaging techniques and tumor detection methods. In this work, an all-optical alternative to the current image based techniques for in vitro minimally invasive procedures has been explored. The technique uses a highly fluorescent marker for the surgical needle to be tracked inside simulated tissues. A series of markers were explored including inorganic (Perovskite and PbS) and organic (carbon dots) nanoparticles and organic dye (Rhodamine 6G) to identify layers of different stiffnesses within a tissue. Rhodamine 6G was chosen based on its high fluorescence signal to track 3D position of a surgical needle in a tissue. The needle was tracked inside homogeneous and inhomogeneous gelatin tissues successfully. This exploratory study of tissue characterization and needle tip tracking using fluorescent markers or photoluminescence technique show potential for real-time application of robot-assisted needle insertions during in vivo procedures.

MRI-Based Brain Tumor Classification using ResNet-50 and Optimized Softmax Regression

Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.

Glioma Identification Based on Digital Multimodal Spectra Integrated With Deep Learning Feature Fusion Using a Miniature Raman Spectrometer.

The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis.

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, and prognosis assessment; however, the DLR on multi-modal glioma imaging has not been assessed. To assess multi-modal MRI for glioma based on the DLR method. We assessed multi-modal images of 107 glioma patients (49 preoperative and 58 postoperative). All the images were reconstructed with both DLR and conventional reconstruction methods, encompassing T1-weighted (T1W), contrast-enhanced T1W (CE-T1), T2-weighted (T2W), and T2 fluid-attenuated inversion recovery (T2-FLAIR). The image quality was evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Visual assessment and diagnostic assessment were performed blindly by neuroradiologists. In contrast with conventionally reconstructed images, (residual) tumor SNR for all modalities and tumor to white/gray matter CNR from DLR images were higher in T1W, T2W, and T2-FLAIR sequences. The visual assessment of DLR images demonstrated the superior visualization of tumor in T2W, edema in T2-FLAIR, enhanced tumor and necrosis part in CE-T1, and fewer artifacts in all modalities. Improved diagnostic efficiency and confidence were observed for preoperative cases with DLR images. DLR of multi-modal MRI reconstruction prototype for glioma has demonstrated significant improvements in image quality. Moreover, it increased diagnostic efficiency and confidence of glioma.

HybridCSF model for magnetic resonance image based brain tumor segmentation

<p>The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convolutional-SVM-fuzzy C-means model combining convolutional neural network (CNN) with the classifier support vector machine (SVM) and clustering technique fuzzy C-means (FCM). The proposed model was implemented on Br35H, BraTs 2020 and BraTs2021 datasets. The suggested model outperformed the existing methods by achieving 98.6% of accuracy on Br35H dataset and dice score of 0.63, 0.87, 0.81 on BraTs 2020 dataset for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively. The achieved dice scores on the BraTs 2021datasets are 0.89, 0.95, and 0.89 for ET, WT, and TC, respectively. The results show that the suggested model HybriCSF outperforms the other CNN-based models in terms of accuracy.</p>

Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors.

Raman spectroscopy (RS) is a promising method for brain tumor detection. Near-infrared autofluorescence (AF) acquired during RS provides additional useful information for tumor identification and was investigated in comparison with RS for delineating brain tumors in situ. Raman spectra were acquired together with AF in situ within the solid tumor and at the tumor border during routine brain tumor surgeries (218 spectra; glioma WHO II-III, n = 6; GBM, n = 10; metastases, n = 10; meningioma, n = 3). Tissue classification for tumor identification in situ was trained on ex vivo data (375 spectra; glioma/GBM patients, n = 20; metastases, n = 11; meningioma, n = 13; and epileptic hippocampi, n = 4). Both in situ and ex vivo data showed that AF intensity in brain tumors was lower than that in border regions and normal brain tissue. Moreover, a positive correlation was observed between the AF intensity and the intensity of the Raman band corresponding to lipids at 1437cm- 1, while a negative correlation was found with the intensity of the protein band at 1260cm- 1. The classification of in situ AF and RS datasets matched the surgeon's evaluation of tissue type, with correct rates of 0.83 and 0.84, respectively. Similar correct rates were achieved in comparison to histopathology of tissue biopsies resected in selected measurement positions (AF: 0.80, RS: 0.83). Spectroscopy was successfully integrated into existing neurosurgical workflows, and in situ spectroscopic data could be classified based on ex vivo data. RS confirmed its ability to detect brain tumors, while AF emerged as a competitive method for intraoperative tumor delineation.

A Novel Approach for Detection, Segmentation, and Classification of Brain Tumors in MRI Images Using Neural Network and Special C Means Fuzzy Clustering Techniques

Brain tumors present a significant health challenge globally, necessitating advanced techniques for accurate detection, segmentation, and classification. This paper presents a comprehensive study focused on the development and evaluation of innovative methodologies for brain tumor analysis using medical imaging data. The primary objective of this research is to enhance the accuracy and efficiency of brain tumor detection, segmentation, and classification processes. To achieve this goal, a multi-step approach is proposed, integrating various computational techniques and machine learning algorithms. First, the study explores novel methods for preprocessing medical imaging data to enhance image quality and reduce noise artifacts. This preprocessing step plays a crucial role in improving the subsequent analysis stages' accuracy and reliability. Next, a robust tumor detection algorithm is developed, leveraging advanced image processing techniques and deep learning models. The proposed algorithm effectively identifies tumor regions within brain images with high accuracy and minimal false positives. Following tumor detection, a segmentation framework is introduced to precisely delineate tumor boundaries from surrounding healthy tissues. The segmentation algorithm combines traditional image processing methods with state-of-the-art deep learning architectures to achieve accurate and efficient tumor delineation. In this paper, we proposed three novel methods for automatic detection, classification, and segmentation of brain tumors. Comprehensive experiments are conducted on the BRATS dataset and show that the proposed model obtains competitive results. The parameters under study are Accuracy rate, Specificity, and Sensitivity. First approach focuses on classifying cancerous and non-cancerous brain tumors in MRI scans. The system first reads the images and employs a novel fusion method to combine information from various modalities (Flair, T1, T1C, T2) for a more comprehensive picture. This enhances the accuracy of tumor characterization. Following this fusion, the images undergo preprocessing, feature extraction, and classification. The preprocessing stage involves grayscale conversion, binarization, wavelet analysis, and region-of-interest (ROI) calculation. Finally, a robust Neural Network (NN) classification method effectively differentiates cancerous and non-cancerous brain tissue. Performance is evaluated by metrics like accuracy, sensitivity, and specificity. Compared to existing methods, this research system demonstrates superior results, achieving a classification accuracy of 96.61%, sensitivity of 96.66%, and specificity of 96.55%. The Second approach combines Backpropagation Neural Networks (BPNN) with Spatial Fuzzy C-Means (SFCM) clustering for brain tumor analysis. The BPNN classifies brains into normal, benign (non-cancerous abnormality), or malignant (cancerous) categories. SFCM helps pinpoint the exact tumor location and size within the MRI scan through segmentation. A dual-tree complex wavelet transform is employed to extract image features efficiently. This method achieved exceptional results: 99.77% classification accuracy, 99.87% sensitivity (correctly identifying tumors), and 98.69% specificity (correctly identifying healthy tissue). Additionally, the over 90% precision demonstrates the effectiveness of this technique in feature extraction and classification. The experiments demonstrate that BPNN-SFCM successfully segment, and extracts brain tumors from MRI scans. The overall accuracy of BPNN-SFCM is better as compared to other proposed methods and existing methods.

Performance Analysis of Walsh-Hadamard Transform-Based Gabor Filter Feature Extraction Method and GLCM Feature Extraction Method for Brain Tumor Detection

Performance Analysis of Walsh-Hadamard Transform-Based Gabor Filter Feature Extraction Method and GLCM Feature Extraction Method for Brain Tumor Detection

Brain Tumor Segmentation Based on α‐Expansion Graph Cut

ABSTRACTIn recent years, there has been an increased interest in using image processing, computer vision, and machine learning in biological and medical imaging research. One area of this interest is the diagnosis of brain tumors, which is considered a difficult and time‐consuming task traditionally performed manually. In this study, we present a method for tumor detection from magnetic resonance images (MRI) using a well‐known graph‐based algorithm, the Boykov–Kolmogorov algorithm, and the α‐expansion method. This approach involves pre‐processing the MRIs, representing the image positions as nodes, and calculations of the weights between edges as differences in intensity. The problem is formulated as an energy minimization problem and is solved by finding the 0,1 score for the image. Post‐processing is also performed to enhance the overall segmentation. The proposed method is easy to implement and shows high accuracy, precision, and efficiency in the results. We believe that this approach will bring significant benefits to scientists and healthcare researchers in qualitative research and can be applied to various imaging modalities for future research.

A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images

A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images

Intelligent Biogenic Missile for Two-Photon Fluorescence Imaging-Guided Combined Photodynamic Therapy and Chemotherapy in Tumors.

Photodynamic therapy (PDT) is a significant noninvasive therapeutic modality, but it is often limited in its application due to the restricted tissue penetration depth caused by the wavelength limitations of the light source. Two-photon (TP) fluorescence techniques are capable of having an excitation wavelength in the NIR region by absorbing two NIR photons simultaneously, which offers the potential to achieve higher spatial resolution for deep tissue imaging. Thus, the adoption of TP fluorescence techniques affords several discernible benefits for photodynamic therapy. Organic TP dyes possess a high fluorescence quantum yield. However, the biocompatibility of organic TP dyes is poor, and the method of coating organic TP dyes with silica can effectively overcome the limitations. Herein, based on the TP silica nanoparticles, a functionalized intelligent biogenic missile TP-SiNPs-G4(TMPyP4)-dsDNA(DOX)-Aptamer (TGTDDA) was developed for effective TP bioimaging and synergistic targeted photodynamic therapy and chemotherapy in tumors. First, the Sgc8 aptamer was used to target the PTK7 receptor on the surface of tumor cells. Under two-photon light irradiation, the intelligent biogenic missile can be activated for TP fluorescence imaging to identify tumor cells and the photosensitizer assembled on the nanoparticle surface can be activated for photodynamic therapy. Additionally, this intelligent biogenic missile enables the controlled release of doxorubicin (DOX). The innovative strategy substantially enhances the targeted therapeutic effectiveness of cancer cells. The intelligent biogenic missile provides an effective method for the early detection and treatment of tumors, which has a good application prospect in the real-time high-sensitivity diagnosis and treatment of tumors.

Gaussian weighting—based random walk segmentation and DCNN method for brain tumor detection and classification

Gaussian weighting—based random walk segmentation and DCNN method for brain tumor detection and classification

An Integrated Thresholding and Morphological Process with Histogram-based Method for Brain Tumor Analysis and MRI Tumor Detection

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning.
 OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory , power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture.
 METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient.
 RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture.
 CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay.

A novel method for simultaneous detection of hematological tumors and infectious pathogens by metagenomic next generation sequencing of plasma

BackgroundMetagenomic next-generation sequencing (mNGS) is valuable for pathogen identification; however, distinguishing between infectious diseases and conditions with potentially similar clinical manifestations, including malignant tumors, is challenging. Therefore, we developed a method for simultaneous detection of infectious pathogens and cancer in blood samples. MethodsPlasma samples (n = 244) were collected from 150 and 94 patients with infections and hematological malignancies, respectively, and analyzed by mNGS for pathogen detection, alongside human tumor chromosomal copy number variation (CNV) analysis (≥5Mbp or 10Mbp CNV region). Further, an evaluation set, comprising 87 plasma samples, was analyzed by mNGS and human CNV analysis, to validate the feasibility of the method. ResultsAmong 94 patients with hematological malignancy, sensitivity values of CNV detection for tumor diagnosis were 69.15 % and 32.98 % for CNV region 5Mbp and 10Mbp, respectively, with corresponding specificities of 92.62 % and 100 % in the infection group. Area under the ROC curve (AUC) values for 5Mbp and 10Mbp region were 0.825 and 0.665, respectively, which was a significant difference of 0.160 (95 % CI: 0.110–0.210; p < 0.001), highlighting the superiority of 5Mbp output region data. Six patients with high-risk CNV results were identified in the validation study: three with history of tumor treatment, two eventually newly-diagnosed with hematological malignancies, and one with indeterminate final diagnosis. ConclusionsConcurrent CNV analysis alongside mNGS for infection diagnosis is promising for detecting malignant tumors. We recommend adopting a CNV region of 10Mbp over 5Mbp for our model, because of the lower false-positive rate (FPR).

A Study of Brain Tumor detection using MRI images

This study investigates the advantages of an algorithm for detecting brain tumors using magnetic resonance imaging. The thematic analysis demonstrates how the algorithm can be understood and changed through narrative descriptions. The findings highlight areas for improvement, which aids in the direction of future research. Based on unexpected results, the algorithm was improved over time. Even though the study had some restrictions and limitations, this makes the algorithm a versatile tool for detecting brain tumors. This study is an important step toward better understanding algorithmic applications and demonstrates the significance of qualitative insights in shaping the future of brain tumor detection methods.

Advanced Machine Learning for Brain Tumor Detection: Leveraging Region of Interest Analysis in MRI Scans

This study introduces an advanced brain tumor detection method leveraging Region of Interest (ROI) analysis in MRI images to enhance diagnostic accuracy and efficiency. By applying sophisticated image processing and machine learning algorithms, our approach significantly improves the identification of tumor presence with reduced computational demands. The results show marked advancements over traditional methods, offering a promising tool for early and precise brain tumor diagnosis

Brain Cancer

Cancer is a significant threat to public health and a leading cause of morbidity across the globe. Of all cancers, brain cancer can be particularly catastrophic as treatment often fails to achieve the desired degree of effectiveness and diagnosis remains associated with a high mortality rate. Africa, as a continent with resource-limited countries, needs to allocate the necessary proper healthcare infrastructure to significantly reduce cancer rates and improve patient survival. A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.

A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography

BackgroundBreast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography.MethodsHuman learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods.ResultsMost models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD.ConclusionsOur proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing.