Federated multimodal artificial intelligence platform for digital pathology and molecular data integration in gynecologic tumors

Abstract

This platform is a privacy-preserving, federated learning system designed to integrate whole-slide digital pathology images with matched molecular profiling and clinical metadata for improved diagnosis, subtyping, and prognostic estimation of gynecologic tumors. The architecture comprises local institutional nodes that retain raw patient data while participating in distributed model training coordinated by a central orchestration server. Each local node preprocesses whole-slide images into patch-level tensors, extracts visual embeddings via convolutional backbones, and processes molecular vectors (e.g., somatic mutations, expression summaries, copy-number measures) via a molecular encoder. A multimodal fusion module - implemented as an attention- based transformer - integrates image and molecular embeddings into a unified representation used by multi-task heads for classification (histologic subtype, diagnostic label) and regression (risk score). The federated learning controller aggregates encrypted model updates (FedAvg) and returns improved global weights without exchanging raw data, enabling cross-site generalization while preserving patient privacy. Explainability components generate attention maps and tile-level saliency (Grad-CAM style) linked to molecular features, providing interpretable morpho- molecular correlations to pathologists. The platform supports API integration with PACS / LIMS, conforms to privacy standards via optional differential privacy and secure aggregation layers, and is extensible to additional omics modalities or transfer / fine-tuning workflows for related tumor types. By combining multimodal fusion, federated training, and clinician-facing interpretability, the system accelerates robust, generalizable AI for precision pathology in gynecologic oncology.

Description

[0001] Federated Multimodal Artificial Intelligence Platform for Digital Pathology and Molecular Data Integration in Gynecologic Tumors

[0002] TECHNICAL FIELD

[0003] The present invention pertains to the field of computational pathology, biomedical informatics, and artificial intelligence in oncology. More particularly, it relates to a federated, multimodal artificial intelligence platform that integrates digital pathology imaging, molecular omics data, and clinical metadata for the diagnosis, classification, and prognostic modeling of gynecologic tumors. The invention combines deep learning architectures, data fusion algorithms, and federated learning frameworks to enable privacy-preserving, cross-institutional training and deployment of diagnostic Al systems. The disclosed technology further supports the interpretation and visualization of histomorphologic-molecular correlations to assist pathologists and oncologists in precision medicine workflows.

[0004] BACKGROUND

[0005] Gynecologic malignancies, including ovarian, endometrial, and cervical cancers, are major causes of morbidity and mortality among women worldwide. Accurate diagnosis and molecular characterization are essential for selecting targeted therapies and predicting clinical outcomes.

[0006] Current diagnostic workflows in pathology heavily rely on manual microscopic examination of hematoxylin and eosin (H&E)-stained slides, complemented by immunohistochemistry (IHC) and selected molecular assays (e.g., BRCA1 / 2, PTEN, PIK3CA). However, these modalities are often performed separately, using fragmented systems that lack interoperability. This fragmentation hinders the comprehensive integration of morphological, molecular, and clinical dimensions that are critical for precision oncology.

[0007] The recent advancement of digital pathology-which converts physical glass slides into high- resolution whole-slide images (WSIs)-has enabled the application of artificial intelligence (Al) for automated feature extraction and classification. Concurrently, next-generation sequencing (NGS) and other molecular profiling technologies have generated large-scale genomic and transcriptomic data that provide deep insights into tumor biology. Despite this, integrating such heterogeneous multimodal data remains a major challenge due to differences in data structure, scale, and representation.

[0008] Moreover, large annotated datasets are essential for training robust Al models, yet data privacy regulations such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States restrict direct sharing of patient data across institutions. As a result, most Al models are trained on small, institution-specific datasets, leading to biased models with poor generalization across populations and imaging conditions.

[0009] To overcome these limitations, federated learning (FL) has emerged as a paradigm that allows multiple institutions to collaboratively train a shared global Al model without exchanging raw data. In this architecture, each participating site trains the model locally and only transmits encrypted model parameters or gradients to a central aggregator, which then updates the global model. This decentralized approach preserves patient confidentiality while enabling large-scale, multi-institutional Al development. However, existing federated learning systems are largely limited to single-modality data such as radiology images or tabular clinical data. There is currently no comprehensive federated Al framework that can simultaneously integrate digital pathology images and molecular omics profiles for gynecologic tumors. Moreover, the interpretability of Al predictions remains an ongoing challenge, particularly in the medical domain where explainable results are required for regulatory and clinical adoption.

[0010] Therefore, there exists an unmet need for a federated, multimodal artificial intelligence platform capable of securely integrating whole-slide pathology images, molecular data, and clinical information across multiple institutions. Such a system should enable robust cross-population learning, provide explainable predictions, and support automated reporting and visualization for pathologists and oncologists. The present invention addresses these technical and clinical gaps through a novel architecture that combines federated learning, multimodal data fusion, and explainable deep neural networks specifically designed for gynecologic oncology applications.

[0011] SUMMARY

[0012] The invention provides a Federated Multimodal Artificial Intelligence Platform designed to analyze, integrate, and interpret digital pathology and molecular datasets associated with gynecologic tumors. The platform employs a federated learning architecture that allows participating hospitals, laboratories, or research centers to train shared Al models locally on their respective datasets. Only encrypted model parameters, not raw patient data, are exchanged between nodes, ensuring compliance with privacy regulations.

[0013] The system incorporates multimodal data streams including:

[0014] 1. Digital pathology images (whole-slide images, cytology, immunohistochemistry);

[0015] 2. Clinical metadata (patient demographics, staging, treatment response).

[0016] An Al inference engine fuses these heterogeneous inputs using attention-based deep neural networks to predict diagnostic categories, histopathologic subtypes, and molecular signatures relevant to gynecologic cancers.

[0017] The invention also includes: o A federated orchestration server for coordinating distributed model training; o A multimodal data harmonization module for aligning digital and molecular data formats; o An explainable- Al (XAI) visualization layer for pathologists to interpret Al-derived heatmaps and genomic associations; and o A clinical reporting interface that generates integrated diagnostic and molecular summaries.

[0018] This system enables robust cross-institutional model generalization, reduces bias from single-site data, and accelerates discovery of novel morpho-molecular correlations in tumors of gynecologic origin.

[0019] DETAILED DESCRIPTION OF THE INVENTION

[0020] In one embodiment, the invention comprises a cloud-orchestrated, federated learning network that connects multiple institutional nodes. Each node includes local databases storing de-identified whole-slide images and matched molecular data from gynecologic tumor cases. A local training agent preprocesses the digital pathology images using convolutional neural networks (CNNs) and extracts image feature embeddings. Molecular data, including geneexpression matrices, somatic mutation lists, and methylation profiles, are processed using graph neural networks (GNNs) and variational autoencoders (VAEs) to capture latent molecular signatures.

[0021] The multimodal fusion layer integrates visual and molecular embeddings through an attentionbased transformer module, enabling the model to learn inter-modal correlations between histomorphologic and genomic patterns.

[0022] The federated learning controller coordinates asynchronous training rounds, aggregates encrypted model gradients from participating nodes, and updates the global model without accessing raw data. Secure aggregation protocols, differential privacy, and homomorphic encryption ensure compliance with privacy regulations.

[0023] An explainable Al component generates interpretable heatmaps overlaying digital slides, highlighting morphological regions contributing most to the prediction, while simultaneously correlating these regions with key molecular alterations such as BRCA1 / 2 mutations, PTEN deletions, or TP53 variants.

[0024] A clinical reporting module integrates predictions into structured diagnostic summaries, including tumor subtype classification, estimated molecular risk group, and suggested therapeutic targets, which can be exported to electronic medical record systems.

[0025] In another embodiment, the platform allows federated transfer learning, enabling adaptation of models trained on ovarian cancer datasets to other gynecologic malignancies, such as endometrial or cervical carcinoma.

[0026] The modular design also supports interoperability with existing digital pathology PACS, laboratory information management systems (LIMS), and oncology decision-support tools.

[0027] 1. Data Ingestion and Preprocessing

[0028] Whole-Slide Image (WSI) Processing: Tiles are extracted (256x256 px) from gigapixel WSIs, normalized using stain normalization (Macenko or Reinhard method), and augmented.

[0029] Molecular Data Processing: Genetic variants (VCF), gene expression, or methylation data are preprocessed into embedding vectors using autoencoders.

[0030] Clinical Metadata: Encoded with categorical embeddings and temporal modeling.

[0031] 2. Model Architecture

[0032] A hybrid model integrating CNNs for local features, Vision Transformers for global contextual reasoning, and Graph Neural Networks for cross-modal fusion. import torch import torch, nn as nn import torch, nn. functional as F class WSIEncoder(nn.Module): def init (self): superQ. init () self.cnn = nn. Sequential) nn.Conv2d(3, 64, 3, stride=l, padding=l), nn.BatchNorm2d(64), nn.ReLUQ, nn.MaxPool2d(2), nn.Conv2d(64, 128, 3, stride=l, padding=l), nn.BatchNorm2d(128), nn.ReLUQ, nn. AdaptiveAvgPool2d(( 1,1))

[0033] ) def forward(self, x): return self.cnn(x).flatten(l) class MolecularEncoder(nn.Module): def init (self, input dim, hidden_dim=256): superQ. init () selffc = nn. Sequential nn.Linear(input_dim, hidden dim), nn.ReLUQ, nn.Linear(hidden_dim, 128)

[0034] ) def forward(self, x): return self.fc(x) class FusionTransformer(nn. Module) : def init (self, d_model=256, nhead=4): superQ. init () selfencoder layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead) self. transformer = nn.TransformerEncoder(self.encoder_layer, num_layers=3) def forward(self, x): return self.transformer(x) class MultimodalPathology Model (nn. Module): def init (self, mol dim): superQ. init () selfwsi encoder = WSIEncoderQ selfmol encoder = MolecularEncoder(mol dim) self fusion = FusionTransformerQ self.fc_out = nn.Linear(256, 3) # Example: normal / low-risk / high-risk def forward(self, wsi, mol): wsi_emb = self. wsi_encoder( wsi) mol emb = selfmol encoder(mol) fused = torch. stack([wsi_emb, mol emb], dim=0) fused = self.fusion(fused) out = selffc_out(fused.mean(0)) return F.softmax(out, dim=l)

[0035] 3. Federated Training

[0036] Training occurs across multiple hospitals or pathology labs without transferring raw data. The local models are trained independently and periodically aggregated by a central coordinator. def federated average(models): global model = models [0] with torch. no gradQ: for key in global_model.state_dict().keys(): global model. state dictQ [key] . copy_( torch.mean(torch.stack([m.state_dict()[key] for m in models]), dim=0)

[0037] ) return global model

[0038] 4. Explainability Layer

[0039] Class activation maps (Grad-CAM) and attention visualization are generated to interpret model predictions, ensuring regulatory transparency.

[0040] 5. Integration and Deployment

[0041] Containerized Services: Each module (ingestion, inference, aggregation) runs in isolated Docker containers.

[0042] APIs: REST / GraphQL APIs for LIS / PACS interoperability.

[0043] Py Torch code (modular, illustrative)

[0044] '"python

[0045] # file: multimodal_federated_platform.py II II II

[0046] Federated Multimodal Al Platform - PyTorch prototype skeleton

[0047] Components:

[0048] - Image encoder (based on torchvision ResNet backbone)

[0049] - Molecular encoder (MLP over vectorized molecular features)

[0050] - Multimodal fusion (Transformer encoder)

[0051] - Classifier heads (diagnosis / subtype, molecular prediction, risk score regression)

[0052] - Federated learning skeleton (Client / Server, FedAvg)

[0053] - Explainability helper (Grad-CAM style for image branch) from typing import Diet, Tuple, List, Optional import copy import math import random import torch import torch, nn as nn import torch, nn. functional as F from torch. utils. data import Dataset, DataLoader

[0054] DEVICE = torch. device("cuda" if torch. cuda.is_available() else "cpu")

[0055] Utility: Weight init def init_weights(m): if isinstance(m, nn.Linear): nn. init. xavier_uniform_(m. weight) if m.bias is not None: nn. init. zeros_(m. bias)

[0056] 1) Image encoder (backbone + projection) class ImageEncoder(nn. Module): def init (self, backbone name: str = "resnetl8", pretrained: bool = True, out dim: int = 512): superQ. init () if backbone name == "resnetl8": backbone = models. resnetl8(pretrained=pretrained) feat dim = backbone, fc.in features

[0057] # remove final fc modules = list(backbone.children())[:-l] # remove pooling & fc self. backbone = nn.Sequential(*modules) # output: (B, feat dim, 1, 1) else: raise NotImplementedError("Only resnetl8 implemented in this prototype") self, pool = nn.AdaptiveAvgPool2d((l, 1)) selfproj = nn.Linear(feat_dim, out dim) selfbn = nn.LayerNorm(out dim) self.apply(init_weights) def forward(self, x: torch. Tensor) -> torch. Tensor:

[0058] # x: (B, C, H, W) feat = self.backbone(x) # (B, feat_dim, 1, 1) feat = selfpool(feat).view(x.size(0), -1) # (B, feat_dim) z = selfproj (feat) z = self.bn(z) z = F.relu(z) return z # (B, out_dim)

[0059] 2) Molecular encoder (vector MLP) class MolecularEncoder(nn.Module): def init (self, input dim: int, hidden dims: List[int] = [256, 128], out dim: int = 256, dropout: float = 0.1): superQ. init () layers = [] prev = input dim for h in hidden dims: layers . append(nn. Linear (prev, h)) layers . append(nn. Lay erN orm(h)) layers. append(nn.ReLU(inplace=True)) layers. append(nn. Dropout(dropout)) prev = h layers . append(nn. Linear(prev, out dim)) layers . append(nn. Lay erN orm(out dim)) self.net = nn. Sequential)* layers) self.apply(init_weights) def forward(self, x: torch. Tensor) -> torch. Tensor:

[0060] # x: (B, input_dim) return F.relu(self.net(x)) # (B, out dim)

[0061] 3) Multimodal fusion: Transformer encoder class FusionTransformer(nn. Module) : def init (self, img_dim: int = 512, mol_dim: int = 256, hidden_dim: int = 512, n_layers: int = 4, n heads: int = 8, dropout: float = 0.1): super)). init ()

[0062] # project each modality to shared embedding dim selfembed dim = hidden dim self.img_proj = nn.Linear(img_dim, hidden dim) self.mol_proj = nn.Linear(mol_dim, hidden dim)

[0063] # pos embedding for 2 tokens (image, molecular) selfpos emb = nn.Parameter(torch.zeros(2, hidden dim)) encoder layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=n_heads, dim_feedforward=hidden_dim * 4, dropout=dropout, activation- 'gelu") self. transformer = nn.TransformerEncoder(encoder_layer, num_layers=n_layers) selfnorm = nn.LayerNorm(hidden dim) self.apply(init_weights) def forward(self, img z: torch. Tensor, mol z: torch. Tensor) -> torch. Tensor:

[0064] # img z: (B, img dim), mol z: (B, mol dim) bsize = img z.size(O) t_img = self.img_proj(img_z) # (B, hidden) t mol = self.mol_proj(mol_z) # (B, hidden)

[0065] # stack as sequence length 2: seq_len=2, batch first for transformer? PyTorch transformer expects (S, B, E) seq = torch. stack([t_img + self.pos_emb[0], t_mol + self.pos_emb[l]], dim=0) # (2, B, hidden)

[0066] # transformer expects (S, B, E) out = self.transformer(seq) # (2, B, hidden) out = out.mean(dim=0) # (B, hidden) - pooled representation out = self. norm( out) return out # fused embedding (B, hidden)

[0067] 4) Heads: classification / regression class MultiTaskHeads(nn.Module): def init (self, in_dim: int, n_classes_diag: int = 4, n_classes_subtype: int = 6): superQ. init () self.diagnosis_head = nn.Sequential(nn.Linear(in_dim, in_dim / / 2), nn.ReLU(), nn.Linear(in_dim / / 2, n_classes_diag)) self.subtype_head = nn.Sequential(nn. Linear (in dim, in_dim / / 2), nn.ReLUQ, nn.Linear(in_dim / / 2, n_classes_subtype)) self.risk_regressor = nn.Sequential(nn.Linear(in_dim, in_dim / / 2), nn.ReLU(), nn.Linear(in_dim / / 2, 1)) self.apply(init_weights) def forward(self, fused: torch. Tensor) -> Dict[str, torch. Tensor]: return {

[0068] "diag_logits" : selfdiagnosis_head(fused),

[0069] "subtype_logits" : self. subtype_head(fused),

[0070] "risk_score": torch. sigmoid(self.risk_regressor(fused)).squeeze(-l)

[0071] Full multimodal model class MultimodalModel(nn.Module): def init (self, img_backbone="resnetl8", img_out=512, mol_in_dim=1024, mol_out=256, fusion_hidden=512, kwargs): superQ. init () selfimage encoder = ImageEncoder(backbone_name=img_backbone, pretrained=False, out_dim=img_out) selfmol encoder = MolecularEncoder(input_dim=mol_in_dim, out_dim=mol_out) self fusion = FusionTransformer(img_dim=img_out, mol_dim=mol_out, hidden_dim=fusion_hidden) self, heads = Multi TaskHeads(in_dim=fusion_hidden, n_classes_diag=2, n_classes_subtype=4) # example dims def forward(self, image: torch. Tensor, mol vec: torch. Tensor) -> Dict[str, torch. Tensor]: img z = selfimage encoder(image) mol z = selfmol encoder(mol vec) fused = self.fusion(img_z, mol z) outputs = selfheads(fused) return outputs

[0072] Dataset placeholders class GyneDataset(Dataset): def init (self, image tensors: List[torch. Tensor], mol vectors: List[torch. Tensor], labels: List[Dict]): assert len(image tensors) == len(mol vectors) == len(labels) self, images = image_tensors selfmols = mol vectors self, labels = labels def len (self): return len(s elf. images) def getitem (self, idx):

[0073] # returns (image, mol_vector, label_dict) return self. images [idx], selfmols [idx], self. labels [idx]

[0074] Federated skeleton: Client and Server class FedClient: II II II

[0075] Each client holds local data and a local copy of the model.

[0076] It trains locally and sends model params (or updates) to the server. II II II def init (self, client id: int, local dataset: Dataset, model: nn.Module, Ir: float = le-4, local epochs: int = 1, batch size: int = 8): selfclient id = client id self, dataset = local dataset selfmodel = copy.deepcopy(model).to(DEVICE) self. optimizer = torch. optim.Adam(self.model.parameters(), lr=lr) selflocal epochs = local epochs selfbatch size = batch size def local train(self) -> Dict[str, torch. Tensor]: self.model.train() loader = DataLoader(self. dataset, batch_size=self.batch_size, shuffle=True) for epoch in range(self.local epochs): for imgs, mols, labels in loader: imgs = imgs.to(DEVICE) mols = mols.to(DEVICE)

[0077] # labels is a diet; for prototype assume 'diag' and 'risk' diag = torch. tensor([l['diag'] for 1 in labels], dtype=torch.long, device=DEVICE) risk = torch. tensor([l['risk'] for 1 in labels], dtype=tor ch. float, device=DEVICE) out = self, modellings, mols) loss_diag = F.cross_entropy(out["diag_logits"], diag) loss risk = Emse_loss(out["risk_score"], risk) loss = loss_diag + loss_risk self, optimizer. zero_grad( ) loss.backward() self, optimizer. stepQ

[0078] # after local training, return state_dict (or deltas) return {k: v.cpu().detach().clone() for k, v in self.model.state_dict().items()} class FedServer:

[0079] Basic FedAvg server: collects client state_dicts and averages them. II II II def init (self, global model: nn.Module): selfglobal model = global model self, round = 0

[0080] @staticmethod def aggregate(states: List[Dict[str, torch. Tensor]]) -> Dict[str, torch. Tensor]: agg = {} n = len(states) for k in states [0].keys(): stacked = torch.stack([s[k].float() for s in states], dim=0) # (n, ...) agg[k] = torch. mean(stacked, dim=0) return agg def distribute_and_update(self, client states: List[Dict[str, torch. Tensor]]): agg_state = selfaggregate(client_states) self, global model. load state dict(agg state) self, round += 1 Explainability (Grad-CAM simplified for image encoder) class GradCAM: def init (self, model: MultimodalModel, target layer name: str =

[0081] " image encoder, backbone" ) : self, model = model

[0082] # find conv layer: for resnetl 8 last conv is layer4

[0083] # simplified: we will hook the whole backbone output self, activations = None self, gradients = None

[0084] # register hooks

[0085] # NOTE: for demo, hook ImageEncoder.backbone[-l] or appropriate conv layer target layer = self.model.image_encoder.backbone[-l] target_layer.register_forward_hook(self._forward_hook) target_layer.register_full_backward_hook(self_backward_hook) def _forward_hook(self, module, inp, outp):

[0086] # outp shape: (B, C, H, W) self, activations = outp.detachQ def _backward_hook(self, module, grad in, grad out):

[0087] # grad_out is tuple self, gradients = grad_out[0].detach() def generate(self, image: torch. Tensor, mol vec: torch. Tensor, class idx: int = 1) -> torch. Tensor: self.model.eval() image = image.to(DEVICE).unsqueeze(0) mol vec = mol_vec.to(DEVICE).unsqueeze(0) out = self.model(image, mol vec) score = out["diag_logits"][0, class_idx] self, model . zero_grad( ) score. backward(retain_graph=True)

[0088] # global pooling of gradients grads = self.gradients.mean(dim=[2, 3], keepdim=True) # (B, C, 1, 1) cam = (grads * self.activations).sum(dim=l, keepdim=True) # (B, 1, H, W) cam = F.relu(cam) cam = Einterpolate(cam, size=image.shape[-2:], mode- ' bilinear", align_corners=False) cam = cam.squeeze(O).squeeze(O) # (H, W) cam = (cam - cam.min()) / (cam.max() - cam.min() + le-8) return cam.cpuQ

[0089] Example orchestration: instantiate server, clients, run federated rounds def example_federated_run(num_clients=3, rounds=3): # placeholder: create global model global model = MultimodalModel(img_backbone="resnetl8", img_out=512, mol_in_dim=1024, mol_out=256, fusion_hidden=512).to(DEVICE) server = FedServer(global_model=global_model)

[0090] # create dummy clients with random data clients = [] for i in range(num clients):

[0091] # create small synthetic data for demo n = 16 imgs = [torch. randn(3, 224, 224) for > in range(n)] mols = [torch. randn( 1024) for > in range(n)] labels = [{"diag": random. randint(0, 1), "risk": random. randomQ} for > in range(n)] ds = GyneDataset(imgs, mols, labels) client = FedClient(client_id=i, local_dataset=ds, model=global_model, local_epochs=l, batch_size=4) clients. append(client) for r in range(rounds): print(f ' >==== Round {r} ====") client states = [] for c in clients:

[0092] # distribute global weights to client c.model.load_state_dict(server.global_model.state_dict()) state = c.local trainQ client states.append(state) server, distribute and update(client states) return server, global model if name == " main " :

[0093] # quick demo run (uses synthetic data) model = example_federated_run(num_clients=2, rounds=2) print("Demo federated run complete.")

Claims

ClaimsWhat is claimed is:

1. A federated, multimodal artificial intelligence system for the integrated analysis of digital pathology and molecular data in gynecologic tumors, comprising: o local data nodes each configured to store whole-slide images, molecular omics data, and clinical information; o a federated learning controller configured to aggregate encrypted model parameters from said local nodes; o a multimodal Al engine comprising convolutional, graph, and transformer neural networks for fusing visual and molecular features; and o a reporting interface for generating integrated diagnostic and prognostic outputs.

2. The system of claim 1, wherein the molecular data comprises genomic, transcriptomic, epigenetic, or proteomic datasets associated with gynecologic tumors.

3. The system of claim 1, wherein data exchange between nodes employs secure aggregation or homomorphic encryption to ensure patient privacy.

4. The system of claim 1, wherein the Al engine produces attention- based visual explanations over digital pathology slides.

5. The system of claim 1 , further comprising a data harmonization module configured to align multimodal datasets across heterogeneous formats.

6. The system of claim 1, wherein federated model updates are orchestrated by a central coordination server without transferring raw data.

7. The system of claim 1, wherein the global model predicts histologic subtype, molecular class, and therapeutic target profiles of gynecologic cancers. A computer-implemented method for analyzing gynecologic tumor data comprising the steps of: receiving multimodal pathology and molecular data; training local Al models at each institution; aggregating encrypted model updates; and generating integrated diagnostic and prognostic reports.

8. The system of claim 1 , wherein federated learning is validated across multiple international sites to enhance model generalizability and reduce bias.