WO2023137627A1 - Système et procédé de modélisation de relation spatiale de micro-environnement tumoral basés sur une image de pathologie numérique - Google Patents

Système et procédé de modélisation de relation spatiale de micro-environnement tumoral basés sur une image de pathologie numérique Download PDF

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WO2023137627A1
WO2023137627A1 PCT/CN2022/072760 CN2022072760W WO2023137627A1 WO 2023137627 A1 WO2023137627 A1 WO 2023137627A1 CN 2022072760 W CN2022072760 W CN 2022072760W WO 2023137627 A1 WO2023137627 A1 WO 2023137627A1
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distribution
cells
spatial
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秦文健
刁颂辉
何佳慧
侯嘉馨
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深圳先进技术研究院
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
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    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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  • the present invention relates to the technical field of medical image processing, and more specifically, to a system and method for modeling the spatial relationship of tumor microenvironment based on digital pathological images.
  • Tumor tissue is a complex structure composed of cancer cells and surrounding non-cancer cells (such as stromal cells and lymphocytes) forming a tumor microenvironment. Its spatial heterogeneity is very complex. Although weakly supervised learning algorithms can be used to identify and locate cancer cells, lymphocytes, stromal cells, and other types of cells (such as macrophages, T cells, or non-differentiating cells) in digital pathology images, existing methods cannot achieve full expression due to simple distance measurement, cell density statistics, or clustering methods. In addition, due to the abundance of cell types in the tumor microenvironment, the current cell spatial organization relationship constructed by graph neural network cannot be applied to the automatic and comprehensive quantitative analysis of the spatial organization distribution of multiple cell types at the same time. Therefore, it is necessary to study new methods for topological clustering of multi-layer networks for multi-cell types.
  • the tumor microenvironment controls the formation, development, metastasis, and drug resistance of solid tumors. It is the result of the interaction between tumor cells and non-tumor cells and tissues such as stromal cells and immune cells to generate anti-tumor immune responses. There are strong clinical and experimental evidences to support the importance of the tumor microenvironment in cancer progression and mediation of drug resistance. However, the relationship of complex anatomy and local microenvironment to metabolic and immune responses remains to be deeply explored. It is difficult for pathologists to capture the interaction between the tumor and its microenvironment through conventional qualitative or semi-quantitative parametric visual inspection.
  • the use of digital pathological image computing analysis to decipher the characteristics of the tumor microenvironment, especially the spatial heterogeneity within the tumor not only provides a new way of thinking for solving the problem of tumor microenvironment analysis, but more importantly, it can mine potential biomarkers related to cancer treatment, so as to design the most appropriate precision medicine treatment plan for patients.
  • computational pathology can not only assist pathologists to examine patients' histological data in a high-throughput, quantitative and objective manner, but also use various types of cells obtained by automatic detection algorithms to construct a spatial relationship map of cells in the tumor, and combine spatial analysis methods to achieve accurate assessment of tumor treatment response and prognosis.
  • the initial research work on the spatial analysis of the tumor microenvironment based on digital pathology usually uses clustering algorithms to perform spatial positioning and morphometric measurement of cell features extracted from digital pathology images to describe the relationship between the spatial distribution pattern of immune cells and diseases.
  • a convolutional neural network was first used to identify tumor-infiltrating lymphocytes (TIL) and segment tumor necrosis regions, then an affine clustering algorithm was used to model the spatial pattern of infiltrating lymphocytes, and then the corresponding clustering features were extracted to describe the spatial pattern of TIL, revealing the relationship between TIL patterns and immune subtypes, tumor types, immune cell fragments, and patient survival.
  • TIL tumor-infiltrating lymphocytes
  • an affine clustering algorithm was used to model the spatial pattern of infiltrating lymphocytes
  • the corresponding clustering features were extracted to describe the spatial pattern of TIL, revealing the relationship between TIL patterns and immune subtypes, tumor types, immune cell fragments, and patient survival.
  • KunHuang et al. used the topological space modeling of deep learning features based on delaunay triangulation graphs.
  • stacked autoencoder networks were used to learn high-level semantic features of cells, and then K-means clustering was used to obtain the spatial pattern of cell nuclei.
  • the statistical method of edge histograms confirmed that the spatial topological features of the renal tumor microenvironment were significantly correlated with survival. They also verified that topological features have superior performance compared with clinical features and cell morphological features in terms of survival prediction.
  • Guanghua Xiao et al. used the method of cell statistical density to construct a regional spatial organization map, used a deep convolutional network to automatically identify cell types, and finally calculated two spatially distributed features to predict the survival of lung cancer patients.
  • the tumor microenvironment is very complex and has spatial heterogeneity.
  • Existing methods cannot be fully expressed by simple distance measurement, cell statistics or clustering methods.
  • the spatial analysis of graphs still relies on manual extraction of features such as the number of adjacent node connections and edge histograms in graphs, and can only analyze simple relationships between cells. Due to the abundance of cell types in the tumor microenvironment, it is difficult for current spatial analysis methods to simultaneously perform fully automatic and comprehensive quantitative analysis of the spatial organization distribution of various types of components (including blood vessels and other structures, lymphocytes, stromal cells and other different types of cells).
  • the purpose of the present invention is to overcome the defects of the above-mentioned prior art, and provide a tumor microenvironment spatial relationship modeling system and method based on digital pathological images, which is a new technical solution for multi-layer network topology clustering of multi-cell types.
  • a tumor microenvironment spatial relationship modeling system based on digital pathological images includes:
  • Image staining standardization module used to determine the pixel distribution type of the pathological image, perform color standardization on the change of staining distribution according to the overall distribution of each pixel of the pathological image, and obtain a staining standardized image;
  • Structural region segmentation module for the dyed standardized image, using a weakly supervised deep learning model to detect the region of interest, and then segment to obtain the target structural region;
  • Cell detection module used to extract various types of cell information from the obtained target structure region
  • Spatial relationship building module it is used to model the multi-layer network by using a multi-layer graph to characterize the co-space distribution among various types of cells, and perform cluster analysis on the multi-layer graph to obtain a quantitative model of spatial distribution, wherein the quantitative model of spatial distribution is used to quantitatively characterize the interaction between tumor cells and the tumor microenvironment.
  • a method for modeling the spatial relationship of tumor microenvironment based on digital pathological images includes the following steps:
  • Step S1 Determine the pixel distribution type of the pathological image, perform color standardization on the change of the staining distribution according to the overall distribution of each pixel in the pathological image, and obtain a staining standardized image;
  • Step S2 For the stained and standardized image, use a weakly supervised deep learning model to detect the region of interest, and then segment to obtain the target structure region;
  • Step S3 extracting various types of cell information from the obtained target structure region
  • Step S4 use a multi-layer graph to model a multi-layer network to characterize the co-space distribution among various types of cells, and perform cluster analysis on the multi-layer graph to obtain a spatial distribution quantitative model, wherein the spatial distribution quantitative model is used to quantitatively characterize the interaction between tumor cells and the tumor microenvironment, the multi-layer graph includes intra-layer relationships and inter-layer interactions, nodes in the same layer represent cells of the same type, and connections between different layers represent spatial connection relationships between different types of cells or structures.
  • the present invention has the advantage that due to the richness and spatial heterogeneity of tumor cell types, there is a strong spatial correlation between various components of the tumor microenvironment and cancer cells at the same time.
  • the present invention provides a mathematical model for constructing a topological space of tumor cells and multiple components of the tumor microenvironment, which can reveal the correlation between intra-tumor heterogeneity and the spatial distribution of cells and tissues in the tumor microenvironment, and provide a new idea for quantitative analysis of tumor evolution mechanisms.
  • Fig. 1 is an architecture diagram of a tumor microenvironment spatial relationship modeling system based on digital pathological images according to an embodiment of the present invention
  • Fig. 2 is a flow chart of a method for modeling the spatial relationship of tumor microenvironment based on digital pathological images according to an embodiment of the present invention.
  • the provided digital pathology image-based tumor microenvironment spatial relationship modeling system includes an image staining standardization module, a structural region segmentation module, a cell detection module and a spatial relationship building module.
  • the staining standardization module is used to solve the problem of inconsistent color distribution of different slices
  • the structural region segmentation module is used to combine the multi-scale imaging characteristics of pathological images, and realize the segmentation of lesion regions and structures at high resolution through regularizable weakly supervised learning methods
  • the cell detection module is used to detect and identify various types of cells in clusters of small targets
  • the spatial relationship construction module is used to identify immune cell types through image registration algorithms, and build a multi-type structure-topological spatial relationship model between cells and tumor cells through a multi-layer graph network to achieve quantitative analysis of the tumor microenvironment.
  • the distribution of stained pixels in pathological images usually conforms to a Gaussian distribution or a partial normal distribution, which can be determined by a self-supervised algorithm based on parameter estimation of the distribution model, where the probability density function (PDF) of the multivariate partial normal distribution is:
  • a is the element of the upper triangular part of the matrix ⁇
  • ⁇ d ( ⁇ ;u, ⁇ ) is the PDF and covariance matrix of a d-variable Gaussian distribution with a ⁇ -mean vector
  • ( ⁇ ) refers to the cumulative distribution function of ⁇ d ( ⁇ ;u, ⁇ )) as a standard univariate Gaussian distribution.
  • the probability density function of the multivariate mixed Gaussian distribution is:
  • the parameter ⁇ d is called the mixing coefficient (mixing coefficients), And 0 ⁇ d ⁇ 1. is the prior probability of the selected kth distribution, density is the probability of x given the kth distribution.
  • the Jarque-Bera test can be used to confirm the actual distribution model type of the pixels of the pathological image, such as testing based on the skewness and kurtosis of the pixel data.
  • the method of depth convolution can be used to estimate and update the parameters of the model, so as to obtain the overall distribution of each pixel of the pathological image, and finally realize the color standardization of the image to be analyzed through the coloring distribution change model.
  • the structural region segmentation module sequentially performs regularized encoding, regularized decoding, weakly supervised learning, and region-of-interest detection through the multi-colored standardized image to obtain the reserved segmentation map of the structural region.
  • one of the key issues is to extract enough key feature codes with limited information to effectively assist segmentation.
  • the characteristics of the key areas in the data are constructed, irrelevant redundant information and noise are removed, and the original data information is abstracted into two types of data matrices.
  • the target structure information is a low-rank matrix, and the redundant and noise information is a sparse matrix.
  • the two matrices are solved separately, and finally the characteristic information of the target structure data is obtained.
  • This method is used in the multi-scale pathological image segmentation model, and a regularizer is designed for model training for the above losses.
  • l(S, Y) is the loss between the real value and the predicted value
  • R(S) is the regularization loss
  • the parameter S f ⁇ (I) ⁇ [0,1]
  • ⁇ and ⁇ are the corresponding item weight parameters set.
  • the input of the model can be fused with image information of multiple magnifications to achieve different attention to cells under high magnification and tissues at medium and low magnifications, so as to fully consider the specificity and generality of the data samples and learn the key features of the data.
  • the diagnostic process of clinical pathologists is simulated, and different attention weights are given to image features of different magnifications, so as to fully consider the data characteristics of images under various magnifications.
  • the optimization function of the corresponding multi-rate regularization loss is:
  • I d represents the image input under d magnification
  • f ⁇ represents the feature calculation under the ⁇ parameter and the attention weight of ⁇
  • ⁇ is mainly calculated by Softmax(f ⁇ (I)).
  • the weighted category activation map is obtained by fusion calculation of the features of multiple scales, and the target tissue and structural area can be obtained after post-processing.
  • the structural region segmentation network can use multiple types such as AlexNet, VGG, GoogleNet, ResNet, etc., which is not limited in the present invention.
  • the key discriminant matrix which is calculated according to the probability distance of the matrix corresponding to the high-dimensional features of different types of images, and using the tumor region as a reference, that is, the distance between cancer and other distances is long
  • the key discriminant matrix which is calculated according to the probability distance of the matrix corresponding to the high-dimensional features of different types of images, and using the tumor region as a reference, that is, the distance between cancer and other distances is long
  • the transformation network based on self-attention realizes the encoding and decoding calculation of each cell and structure in the image, and at the same time combines the key discriminant matrix for fusion analysis, as follows:
  • W A is a learnable weight and H, W, and C respectively represent the isotropic dimensions of the features, L represents the number of visual markers T, and L ⁇ HW.
  • the self-attention transformation is used to model the dependency between T, and projected to the dimension of the normal feature map, combined with the key discriminant matrix G of the preorder, expressed as:
  • X out X in +SOFTMAX L ((X in W Q )(TW K ) T )T+G (6)
  • Xin represents the image features obtained during the multi-scale pathological image tumor region detection
  • X out represents the final output result of the cell detection module
  • W Q and W K are the weight parameters that can be learned, respectively, after constructing the feature relationship of the image, a large amount of data learning is carried out to realize the recognition and positioning of different types of cells and structures.
  • the spatial relationship building module sequentially performs processes such as image slicing, image feature encoding, low-magnification rigid registration, high-magnification non-rigid registration, multi-layer graph network construction, graph embedding dimensionality reduction, point cloud distribution data acquisition, continuous coherent modeling and feature cluster analysis, and finally obtains a quantitative model of spatial distribution.
  • processes such as image slicing, image feature encoding, low-magnification rigid registration, high-magnification non-rigid registration, multi-layer graph network construction, graph embedding dimensionality reduction, point cloud distribution data acquisition, continuous coherent modeling and feature cluster analysis, and finally obtains a quantitative model of spatial distribution.
  • the following focuses on the multi-layer graph network construction and feature cluster analysis.
  • a multi-layer graph is used to model a multi-layer network.
  • a multi-layer graph is a collection of single-layer graph adjacency matrices with weights, including intra-layer relationships and inter-layer interactions.
  • the specific implementation includes the multi-layer network constructed based on the multi-layer graph and the clustering calculation for the multi-layer network, so as to finally realize the construction of the spatial distribution expression model between the tumor cells and the multi-components of the tumor microenvironment.
  • a cross-layer adjacency matrix set C p ⁇ A l,k ,k ⁇ l ⁇ can be obtained, which represents the edges between nodes of different layers, and p represents the number of connection graphs.
  • a multi-layer network A collection of interlayer connections that connect nodes across layers for sides There are u ⁇ V(G k ) and v ⁇ V(G l ), and k ⁇ l. defined multilayer network
  • the hyperadjacency matrix of has a block matrix structure:
  • nodes in the same layer are defined to represent the same type of cells, and connections between different layers are defined to represent the spatial connection relationship between different types of cells or structures. Taking vascular structures and tumor cells as an example, the relationship between cell-structure layers can be established based on the spatial distance to obtain the value of the off-diagonal element A kl between layers. Tumors or immune cells that are close to blood vessels have a strong connection with the structural layer, and vice versa; the diagonal elements are intra-layer matrices that are also obtained through the Euclidean distance between cells.
  • the network After building a multi-layer graph network, considering that extracting meaningful information from a complex network requires a lot of computation and memory, in order to solve these two problems, the network is transformed into a low-dimensional space through node embedding and its structural information is preserved, for example, the method of graph embedding is used to achieve dimensionality reduction.
  • topological data analysis topological data analysis
  • the evaluation of changes in network topology induction is used to detect persistent features over a wide range of thresholds ⁇ j .
  • the goal is to detect persistent features that exceed different thresholds ⁇ , and this persistent feature is a feature of the internal spatial organization distribution.
  • This persistent graph clustering algorithm can obtain more accurate clustering results.
  • the multi-layer network clustering method adopted in the embodiment of the present invention starts from the perspective of similarity in the shape of multi-resolution recorded data, and performs clustering calculations on multi-layer networks under unsupervised conditions.
  • the multi-lens tool of TDA is introduced in the clustering calculation, whose core idea is that if the local neighborhoods of two points are similar in shape at all resolution scales, then the distance between them is close enough to be clustered into a cluster. Therefore, persistent graph clustering utilizes the distance function and local spatial information around points, and can obtain more accurate clustering results for multi-layer graph networks.
  • the present invention also provides a method for modeling the spatial relationship of the tumor microenvironment based on digital pathological images, which is used to realize the functions of each module in the above system.
  • the method includes: step S110, determining the pixel distribution type of the pathological image, performing color standardization on the change of the staining distribution according to the overall distribution of each pixel in the pathological image, and obtaining a staining standardized image; step S120, using a weakly supervised deep learning model to detect the region of interest for the staining standardized image, and then segmenting the target structure region; step S130, extracting various types of cell information from the obtained target structure region; Class analysis to obtain a quantitative model of spatial distribution.
  • the spatial distribution quantitative model is used to quantitatively characterize the interaction between tumor cells and the tumor microenvironment
  • the multi-layer graph includes intra-layer relationships and inter-layer interactions
  • nodes in the same layer represent cells of the same type
  • connections between different layers represent spatial connection relationships between different types of cells or structures.
  • the present invention has at least the following technical effects:
  • a fast calculation method for multi-scale pathological images based on learnable regularization constraint encoding and decoding weakly supervised learning is designed. Aiming at the difficulty of calculating a single pathological image with over one billion pixels and the incomplete utilization of information at different magnification scales, combined with the idea of weak supervision and deep learning technology, it does not need to rely on large-scale data labeling and makes full use of cross-scale information to achieve rapid detection of lesion regions of interest and accurate positioning of cell nuclei in digital panoramic pathological images.
  • the self-attention transformation network is used to realize the encoding and decoding of each cell and structure, thereby realizing the rapid detection and accurate identification of clustered multi-type small target cells.
  • the tumor microenvironment topological space modeling method based on persistence graph clustering, to further realize the quantitative calculation of pathological diagnostic indicators.
  • Conventional distance or statistical methods are difficult to analyze the spatial expression of complex tumor microenvironments.
  • the present invention introduces the concept of topological data analysis into complex multi-layer network clustering calculations, and proposes a topological space modeling method for persistent graph clustering. It reveals the correlation between intra-tumor heterogeneity and the spatial distribution of cells and tissues in the tumor microenvironment, and provides a new idea for quantitative analysis of tumor evolution mechanisms.
  • the present invention can be a system, method and/or computer program product.
  • a computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present invention.
  • a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
  • a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read only memory (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanically encoded devices, such as punched cards or raised-in-recess structures with instructions stored thereon, and any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read only memory
  • EPROM or flash memory erasable programmable read only memory
  • SRAM static random access memory
  • CD-ROM compact disc read only memory
  • DVD digital versatile disks
  • memory sticks floppy disks
  • mechanically encoded devices such as punched cards or raised-in-recess structures with instructions stored thereon, and any suitable combination of the foregoing.
  • Computer-readable storage media as used herein is not to be interpreted as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or electrical signals transmitted through electrical wires.
  • Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device.
  • Computer program instructions for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages—such as Smalltalk, C++, Python, etc., and conventional procedural programming languages—such as the “C” language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).
  • electronic circuits such as programmable logic circuits, field programmable gate arrays (FPGAs) or programmable logic arrays (PLAs), can be executed by utilizing state information of computer readable program instructions to personalize electronic circuits that execute computer readable program instructions, thereby implementing various aspects of the present invention.
  • These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine, such that these instructions, when executed by a processor of a computer or other programmable data processing devices, produce devices that implement the functions/actions specified in one or more blocks in the flowchart and/or block diagrams.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause computers, programmable data processing devices and/or other devices to work in a specific manner, so that the computer-readable medium storing instructions includes an article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowchart and/or block diagrams.
  • Computer-readable program instructions can also be loaded onto a computer, other programmable data processing device, or other equipment, so that a series of operation steps are executed on the computer, other programmable data processing device, or other equipment to generate a computer-implemented process, so that the instructions executed on the computer, other programmable data processing device, or other equipment realize the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • each block in the flowchart or block diagrams may represent a module, a program segment, or a portion of instructions comprising one or more executable instructions for implementing specified logical functions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block in the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by special purpose hardware-based systems that perform the specified functions or actions, or by combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by means of hardware, implementation by means of software, and implementation by a combination of software and hardware are all equivalent.

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Abstract

Sont divulgués dans la présente invention un système et un procédé de modélisation de relation spatiale de micro-environnement tumoral basés sur une image de pathologie numérique. Le système comprend un module de normalisation de coloration d'image, configuré pour déterminer un type de distribution de pixel d'une image de pathologie pour effectuer une normalisation de couleur sur le changement de distribution de coloration pour obtenir une image de normalisation de coloration ; un module de segmentation de région de structure, configuré pour détecter une région d'intérêt à l'aide d'un modèle d'apprentissage profond faiblement supervisé destiné à l'image de normalisation de coloration, puis pour segmenter pour obtenir une région de structure cible ; un module de détection de cellule, configuré pour extraire des informations de divers types de cellules à partir de la région de structure cible ; et un module de construction de relation spatiale, configuré pour modéliser un réseau multicouche à l'aide d'un graphe multicouche, pour représenter une distribution co-spatiale parmi les divers types de cellules, et effectuer une analyse de regroupement sur le graphe multicouche pour obtenir un modèle quantitatif de distribution spatiale. La présente invention peut révéler avec précision la corrélation entre l'hétérogénéité intratumorale et la cellule de micro-environnement tumoral et la règle de distribution spatiale de tissu, fournissant ainsi une nouvelle idée d'analyse quantitative pour un mécanisme d'évolution de tumeur.
PCT/CN2022/072760 2022-01-19 2022-01-19 Système et procédé de modélisation de relation spatiale de micro-environnement tumoral basés sur une image de pathologie numérique WO2023137627A1 (fr)

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