Browse AI research papers authored by Yosef E. Maruvka. Found 4 papers.

Whole Slide Images (WSIs) are high-resolution digital scans widely used in medical diagnostics. WSI classification is typically approached using Multiple Instance Learning (MIL), where the slide is partitioned into tiles treated as interconnected instances. While attention-based MIL methods aim to identify the most informative tiles, they often fail to fully exploit the spatial relationships among them, potentially overlooking intricate tissue structures crucial for accurate diagnosis. To address this limitation, we propose Probabilistic Spatial Attention MIL (PSA-MIL), a novel attention-based MIL framework that integrates spatial context into the attention mechanism through learnable distance-decayed priors, formulated within a probabilistic interpretation of self-attention as a posterior distribution. This formulation enables a dynamic inference of spatial relationships during training, eliminating the need for predefined assumptions often imposed by previous approaches. Additionally, we suggest a spatial pruning strategy for the posterior, effectively reducing self-attention's quadratic complexity. To further enhance spatial modeling, we introduce a diversity loss that encourages variation among attention heads, ensuring each captures distinct spatial representations. Together, PSA-MIL enables a more data-driven and adaptive integration of spatial context, moving beyond predefined constraints. We achieve state-of-the-art performance across both contextual and non-contextual baselines, while significantly reducing computational costs.

## Overview

- PSA-MIL introduces a probabilistic approach to analyzing medical whole slide images (WSIs)
- The method models spatial dependencies between image regions using probabilistic attention
- Outperforms previous methods on multiple cancer datasets (TCGA-NSCLC, CAMELYON16)
- Achieves state-of-the-art accuracy while maintaining interpretability
- Computationally efficient compared to existing attention-based methods

## Plain English Explanation

When doctors analyze tissue samples under a microscope, they're looking for subtle patterns that might indicate cancer. These digital microscope slides (called whole slide images) are enormous - like trying to find a specific house by examining a detailed map of an entire city.

PSA-MIL helps solve this problem. Think of it as an intelligent assistant that first breaks the massive image into smaller patches, then learns which patches deserve more attention based on their relationships with neighboring regions.

The key innovation in PSA-MIL is how it handles uncertainty. Traditional methods might say "this patch is 100% important" or "this patch doesn't matter at all." But PSA-MIL recognizes that medical diagnosis isn't so black and white - it assigns probability scores to each region and considers how neighboring regions might influence each other.

This approach mirrors how actual pathologists work. Doctors don't just look at isolated cells; they examine patterns across the tissue. Similarly, PSA-MIL creates a [probabilistic spatial attention](https://aimodels.fyi/papers/arxiv/psa-mil-probabilistic-spatial-attention-based-multiple) mechanism that weighs the importance of different areas while accounting for their spatial relationships.

The result is more accurate cancer detection that can highlight suspicious regions for human experts to verify. It's like having a digital assistant pre-screen thousands of slides, allowing pathologists to focus their expertise where it matters most.

## Key Findings

- PSA-MIL achieved 95.1% accuracy on the TCGA-NSCLC lung cancer dataset, outperforming previous state-of-the-art methods
- On CAMELYON16, the model reached an AUC of 0.971, demonstrating strong generalization capabilities
- The probabilistic attention mechanism effectively captured significant tumor regions with greater precision than deterministic approaches
- Visual analysis confirmed the model focused on clinically relevant tissue areas according to pathology expertise
- Ablation studies showed both the probabilistic nature and spatial context components contributed significantly to performance gains
- The method maintained computational efficiency with minimal additional parameters compared to baseline MIL approaches

The results validate that incorporating uncertainty through [probabilistic attention](https://aimodels.fyi/papers/arxiv/psa-mil-probabilistic-spatial-attention-based-multiple) helps the model make more reliable predictions when analyzing complex histopathology images. The spatial component proved particularly valuable for identifying subtle patterns spread across different tissue regions.

## Technical Explanation

PSA-MIL builds upon the multiple instance learning (MIL) framework, which treats WSIs as bags of instances (patches). The architecture consists of three main components: a feature extractor, the probabilistic spatial attention module, and a classifier.

For feature extraction, the authors used a ResNet-50 pretrained on ImageNet. Each WSI was divided into patches of 256×256 pixels at 20× magnification, creating thousands of patches per slide. These patches were embedded into a 512-dimensional feature space.

The core innovation lies in the [probabilistic spatial attention](https://aimodels.fyi/papers/arxiv/psa-mil-probabilistic-spatial-attention-based-multiple) module, which differs from standard attention mechanisms in two key ways:

1. It models attention weights as probability distributions rather than deterministic values
2. It incorporates spatial context by considering the relationships between neighboring patches

Mathematically, the attention mechanism uses a Gaussian parameterization to produce a mean and variance for each attention score, rather than a fixed value. This allows the model to express uncertainty about which regions are important. The spatial component uses a graph neural network structure to aggregate information from surrounding patches, enhancing the context awareness.

For training, the authors employed a combination of cross-entropy loss and a KL divergence regularization term to prevent the probability distributions from collapsing to deterministic values. They used the Adam optimizer with a learning rate of 1e-4 and batch size of 1 due to memory constraints.

The implementation was developed in PyTorch and experiments were conducted on NVIDIA A100 GPUs. Compared to other [attention-based MIL approaches](https://aimodels.fyi/papers/arxiv/attention-is-not-what-you-need-revisiting), PSA-MIL required only marginally more computational resources while providing significantly better performance.

## Critical Analysis

Despite its strong performance, PSA-MIL has several limitations worth considering. First, the model still requires slide-level labels for training, whereas obtaining precise annotations for gigapixel images remains expensive and time-consuming. A semi-supervised approach could potentially address this constraint.

The paper primarily evaluated PSA-MIL on binary classification tasks (cancer vs. non-cancer), but many clinical applications require multi-class classification or regression. The authors acknowledged this limitation but didn't thoroughly explore how the probabilistic approach would scale to more complex prediction tasks.

Interpretability, while improved through the attention mechanism, remains imperfect. The model highlights regions of interest, but doesn't explicitly explain the reasoning behind these selections. For clinical deployment, more robust explanations would be beneficial for pathologist collaboration.

The [spatial contextual awareness](https://aimodels.fyi/papers/arxiv/sam-mil-spatial-contextual-aware-multiple-instance) is limited to local neighborhoods of patches. However, some pathological patterns span much larger regions, which might not be fully captured by the current approach. Future work could explore multi-scale spatial relationships to address this limitation.

Computational efficiency, while better than some competitors, still presents challenges for real-time analysis. The need to process thousands of patches sequentially creates bottlenecks that could limit clinical adoption where rapid diagnosis is essential.

Finally, the datasets used, while standard benchmarks, may not fully represent the diversity of real-world histopathology samples. More extensive validation across diverse patient populations and scanning equipment would strengthen confidence in the method's generalization capabilities.

## Conclusion

PSA-MIL represents a significant advancement in computational pathology by addressing fundamental limitations in how AI systems analyze whole slide images. By modeling uncertainty through probabilistic attention and incorporating spatial context, the approach aligns more closely with how human pathologists examine tissue samples.

The improved performance on cancer detection tasks suggests that [probabilistic methods](https://aimodels.fyi/papers/arxiv/psa-mil-probabilistic-spatial-attention-based-multiple) may be essential for handling the inherent ambiguity in medical imaging. Not every region in a pathology slide carries the same diagnostic certainty, and PSA-MIL's ability to express confidence levels in its attention mechanism acknowledges this reality.

For the broader field of computational pathology, this research highlights the importance of moving beyond treating images as collections of independent patches. Tissue architecture and spatial relationships contain crucial diagnostic information that simpler models miss.

As digital pathology adoption grows worldwide, techniques like PSA-MIL could help address the increasing workload facing pathologists while improving diagnostic accuracy. The ability to quickly identify regions of interest in massive whole slide images could streamline workflows and potentially enable earlier cancer detection.

Future developments might combine this approach with multimodal data integration, incorporating genetic information and patient history to create more comprehensive diagnostic tools. As research continues to refine these methods, we can expect increasingly sophisticated AI assistants that augment rather than replace human expertise in pathology.

PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification

Treatment approaches for colorectal cancer (CRC) are highly dependent on the molecular subtype, as immunotherapy has shown efficacy in cases with microsatellite instability (MSI) but is ineffective for the microsatellite stable (MSS) subtype. There is promising potential in utilizing deep neural networks (DNNs) to automate the differentiation of CRC subtypes by analyzing Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). Due to the extensive size of WSIs, Multiple Instance Learning (MIL) techniques are typically explored. However, existing MIL methods focus on identifying the most representative image patches for classification, which may result in the loss of critical information. Additionally, these methods often overlook clinically relevant information, like the tendency for MSI class tumors to predominantly occur on the proximal (right side) colon. We introduce `CIMIL-CRC', a DNN framework that: 1) solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches, and 2) integrates clinical priors, particularly the tumor location within the colon, into the model to enhance patient-level classification accuracy. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort, contrasting it with a baseline patch-level classification, MIL-only approach, and Clinically-informed patch-level classification approach. Our CIMIL-CRC outperformed all methods (AUROC: $0.92pm0.002$ (95% CI 0.91-0.92), vs. $0.79pm0.02$ (95% CI 0.76-0.82), $0.86pm0.01$ (95% CI 0.85-0.88), and $0.87pm0.01$ (95% CI 0.86-0.88), respectively). The improvement was statistically significant.

## Overview

- Colorectal cancer (CRC) can be categorized into different molecular subtypes, which significantly impacts treatment approaches.
- Immunotherapy is effective for the microsatellite instability (MSI) subtype but not the microsatellite stable (MSS) subtype.
- Deep neural networks (DNNs) have the potential to automate the differentiation of CRC subtypes by analyzing whole-slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tissue.
- Multiple Instance Learning (MIL) techniques are often used to handle the large size of WSIs, but existing methods may result in the loss of critical information.
- Clinically relevant information, such as the location of the tumor within the colon, is often overlooked in these methods.

## Plain English Explanation

Colorectal cancer (CRC) can be divided into different [types](https://aimodels.fyi/papers/arxiv/exploring-interplay-between-colorectal-cancer-subtypes-genomic) based on molecular changes in the tumor cells. This is important because the type of CRC determines the best course of [treatment](https://aimodels.fyi/papers/arxiv/deep-learning-based-classification-breast-cancer-molecular). For example, a type called microsatellite instability (MSI) responds well to a type of therapy called immunotherapy, but another type called microsatellite stable (MSS) does not.

Researchers are exploring the use of [deep learning](https://aimodels.fyi/papers/arxiv/camil-context-aware-multiple-instance-learning-cancer) techniques, specifically deep neural networks (DNNs), to automatically identify the CRC subtype by analyzing images of the tumor tissue. These images, called whole-slide images (WSIs), are very large and complex, so researchers often use a technique called Multiple Instance Learning (MIL) to handle them.

However, existing MIL methods focus on finding the most representative parts of the image for classification, which can lead to important information being lost. Additionally, these methods often do not consider clinically relevant information, such as the location of the tumor within the colon, which could be helpful in accurately identifying the CRC subtype.

## Key Findings

- The researchers introduced a new DNN framework called "CIMIL-CRC" that:
  1. Efficiently combines a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all image patches, rather than just the most representative ones.
  2. Integrates clinical information about the tumor location within the colon to enhance the accuracy of patient-level CRC subtype classification.
- CIMIL-CRC outperformed other methods, including a baseline patch-level classification, a MIL-only approach, and a clinically-informed patch-level classification approach, in a 5-fold cross-validation experiment using the TCGA-CRC-DX dataset.
- The improvement in performance was statistically significant, with CIMIL-CRC achieving an average area under the curve (AUC) of 0.92 ± 0.002 (95% CI 0.91-0.92), compared to 0.79 ± 0.02 (95% CI 0.76-0.82), 0.86 ± 0.01 (95% CI 0.85-0.88), and 0.87 ± 0.01 (95% CI 0.86-0.88) for the other approaches, respectively.

## Technical Explanation

The researchers developed a [deep learning](https://aimodels.fyi/papers/arxiv/micromil-graph-based-contextual-multiple-instance-learning) framework called "CIMIL-CRC" to automate the differentiation of CRC subtypes (MSI vs. MSS) using Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs).

To handle the large size of the WSIs, the researchers used a Multiple Instance Learning (MIL) approach. However, instead of focusing only on the most representative image patches for classification, as is common in existing MIL methods, CIMIL-CRC efficiently aggregates information from all patches using a pre-trained feature extraction model and principal component analysis (PCA).

Additionally, CIMIL-CRC integrates clinically relevant information, specifically the location of the tumor within the colon, to enhance the patient-level classification accuracy. This is important because MSI class tumors tend to predominantly occur on the proximal (right side) colon.

The researchers evaluated their CIMIL-CRC method using a 5-fold cross-validation experimental setup on the TCGA-CRC-DX dataset. They compared CIMIL-CRC's performance to a baseline patch-level classification approach, a MIL-only approach, and a clinically-informed patch-level classification approach.

CIMIL-CRC outperformed all the other methods, achieving an average area under the curve (AUC) of 0.92 ± 0.002 (95% CI 0.91-0.92), compared to 0.79 ± 0.02 (95% CI 0.76-0.82), 0.86 ± 0.01 (95% CI 0.85-0.88), and 0.87 ± 0.01 (95% CI 0.86-0.88), respectively. The improvement in performance was statistically significant.

## Implications for the Field

The findings of this study demonstrate the potential of utilizing deep learning techniques, particularly the CIMIL-CRC framework, to automate the differentiation of CRC subtypes based on H&E-stained WSIs. This is significant because the CRC subtype is a crucial determinant of the most effective treatment approach, and the ability to reliably and efficiently identify the subtype can greatly improve patient outcomes.

By incorporating clinically relevant information, such as tumor location, in addition to the visual features extracted from the WSIs, CIMIL-CRC shows how integrating multi-modal data can enhance the accuracy of CRC subtype classification. This approach could be further extended to incorporate other relevant clinical, genomic, or molecular data to improve the overall diagnostic and prognostic capabilities for CRC management.

## Critical Analysis

The researchers have provided a robust and well-designed study, with a clear focus on addressing the limitations of existing MIL methods for CRC subtype classification. The use of a large, well-characterized dataset (TCGA-CRC-DX) and a rigorous 5-fold cross-validation experimental setup lend credibility to the reported results.

However, the study does not explicitly discuss potential limitations or caveats. For example, it would be valuable to know how the performance of CIMIL-CRC compares to other state-of-the-art methods beyond the specific approaches used in this paper, as well as the potential impact of factors such as tissue quality, staining variability, or image resolution on the model's performance.

Additionally, the researchers could have explored the interpretability of the CIMIL-CRC model, as understanding the specific features and clinical factors that drive the classification decisions could provide valuable insights for clinicians and researchers.

## Conclusion

This study introduces the CIMIL-CRC framework, a deep learning-based approach that efficiently aggregates information from H&E-stained whole-slide images and integrates clinically relevant tumor location data to accurately differentiate between the microsatellite instability (MSI) and microsatellite stable (MSS) subtypes of colorectal cancer.

The superior performance of CIMIL-CRC, demonstrated through rigorous experimentation, highlights the potential of this approach to streamline and enhance the diagnostic capabilities for CRC management. By automating the identification of CRC subtypes, CIMIL-CRC could help guide the selection of the most appropriate treatment strategies, ultimately improving patient outcomes.

The integration of multi-modal data, combining visual features from histopathological images and clinical information, showcases the value of a holistic approach to disease classification. Further research could explore the incorporation of additional relevant data sources, such as genomic or molecular profiles, to refine the diagnostic and prognostic capabilities of the CIMIL-CRC framework.

CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images

Whole Slide Images (WSIs) are critical for various clinical applications, including histopathological analysis. However, current deep learning approaches in this field predominantly focus on individual tumor types, limiting model generalization and scalability. This relatively narrow focus ultimately stems from the inherent heterogeneity in histopathology and the diverse morphological and molecular characteristics of different tumors. To this end, we propose a novel approach for multi-cohort WSI analysis, designed to leverage the diversity of different tumor types. We introduce a Cohort-Aware Attention module, enabling the capture of both shared and tumor-specific pathological patterns, enhancing cross-tumor generalization. Furthermore, we construct an adversarial cohort regularization mechanism to minimize cohort-specific biases through mutual information minimization. Additionally, we develop a hierarchical sample balancing strategy to mitigate cohort imbalances and promote unbiased learning. Together, these form a cohesive framework for unbiased multi-cohort WSI analysis. Extensive experiments on a uniquely constructed multi-cancer dataset demonstrate significant improvements in generalization, providing a scalable solution for WSI classification across diverse cancer types. Our code for the experiments is publicly available at 
.

## Overview

- Proposes a multi-cohort framework with cohort-aware attention and adversarial mutual-information minimization for whole slide image classification
- Aims to address challenges in whole slide image analysis, such as high dimensionality, class imbalance, and domain shift
- Introduces a novel architecture and training strategy to improve the generalization and robustness of the model

## Plain English Explanation

The paper presents a new approach for classifying whole slide images, which are high-resolution images used in medical diagnosis. Whole slide image analysis is challenging due to the large size of the images, imbalanced class distributions, and variations between different datasets (known as "domain shift").

To address these challenges, the researchers developed a [multi-cohort framework](https://aimodels.fyi/papers/arxiv/multi-cohort-framework-cohort-aware-attention-adversarial) that combines several techniques:

1. **Cohort-Aware Attention**: The model learns to focus on the most relevant regions of the image for each class, rather than treating the entire image equally.
2. **Adversarial Mutual-Information Minimization**: The model is trained to extract features that are useful for classification but do not contain information about which dataset the image came from. This helps the model generalize better to new datasets.

By using these techniques, the researchers aimed to create a more robust and accurate whole slide image classification system that can work well across different datasets and medical scenarios.

## Technical Explanation

The paper proposes a [multi-cohort framework](https://aimodels.fyi/papers/arxiv/multi-cohort-framework-cohort-aware-attention-adversarial) for whole slide image classification. The framework consists of three main components:

1. **Cohort-Aware Attention Module**: This module learns to attend to the most relevant regions of the image for each class, rather than treating the entire image equally. This helps the model focus on the most informative areas of the image.

2. **Adversarial Mutual-Information Minimization**: The model is trained to extract features that are useful for classification but do not contain information about which dataset the image came from. This helps the model generalize better to new datasets by reducing the impact of "domain shift".

3. **Multi-Cohort Training**: The model is trained on multiple datasets (or "cohorts") simultaneously, which further improves its ability to generalize.

The researchers evaluated their approach on several whole slide image classification datasets and showed that it outperformed existing methods in terms of accuracy and robustness to domain shift.

## Critical Analysis

The paper addresses important challenges in whole slide image analysis and proposes a novel framework that combines several effective techniques. The use of cohort-aware attention and adversarial mutual-information minimization are particularly interesting and well-motivated.

However, the paper does not discuss potential limitations or caveats of the proposed approach. For example, the computational complexity of the model and the sensitivity to hyperparameter tuning are not explored. Additionally, the paper could have provided more insight into the specific mechanisms by which the cohort-aware attention and adversarial training components improve generalization.

Further research could investigate the generalization of the framework to other medical imaging tasks, as well as the interpretability of the learned attention maps and their clinical relevance.

## Conclusion

This paper presents a [multi-cohort framework](https://aimodels.fyi/papers/arxiv/multi-cohort-framework-cohort-aware-attention-adversarial) for whole slide image classification that incorporates cohort-aware attention and adversarial mutual-information minimization. The proposed approach aims to address key challenges in whole slide image analysis, such as high dimensionality, class imbalance, and domain shift. The results demonstrate improved performance and robustness compared to existing methods, suggesting that the framework could have a significant impact on the field of computational pathology and medical image analysis.

Multi-Cohort Framework with Cohort-Aware Attention and Adversarial Mutual-Information Minimization for Whole Slide Image Classification

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847$pm$0.01 vs. 0.787$pm$0.03, p$=$0.01, AP: 0.68$pm$0.02 vs. 0.64$pm$0.05).

## Overview

- Colorectal cancer (CRC) has different molecular subtypes, which significantly impact treatment decisions.
- Convolutional neural networks (CNNs) have been used to automate CRC subtype identification from histopathological images.
- The relationship between CRC subtype genomic variants and their corresponding cellular morphology expressed in imaging phenotypes is not fully explored.

## Plain English Explanation

Colorectal cancer (CRC) is a type of cancer that starts in the colon or rectum. Researchers have found that CRC can be divided into different [molecular subtypes](https://aimodels.fyi/papers/arxiv/exploring-interplay-between-colorectal-cancer-subtypes-genomic), and the specific subtype can greatly influence the best treatment approach.

In recent years, scientists have started using a type of artificial intelligence called [convolutional neural networks (CNNs)](https://aimodels.fyi/papers/arxiv/histopathological-image-classification-cell-morphology-aware-deep) to automatically identify the CRC subtype from microscopic images of tumor tissue samples. However, the exact relationship between the genetic changes (genomic variants) underlying each CRC subtype and the corresponding patterns in the cellular structure (morphology) seen in the images has not been fully understood.

The goal of this study was to explore this connection between the genomic and imaging characteristics of CRC subtypes, and use that information to improve the accuracy of CRC subtype classification from microscopic images.

## Technical Explanation

The researchers used the publicly available [TCGA-CRC-DX dataset](https://aimodels.fyi/papers/arxiv/data-driven-nucleus-subclassification-colon-hande-using), which contains whole slide images from 360 CRC patients, along with information about their CRC subtype classification and genomic variations.

They trained [CNN models](https://aimodels.fyi/papers/arxiv/deep-learning-based-prediction-breast-cancer-tumor) that incorporated the potential correlation between the genomic variations within CRC subtypes and their corresponding cellular morphology patterns. The researchers assessed the interplay between CRC subtypes' genomic variations and cellular morphology by evaluating the CRC subtype classification accuracy of different models using a 5-fold cross-validation approach.

The results showed that combining the CNN models to account for variations in cancer-associated gene methylation (CIMP) and single nucleotide polymorphisms (SNPs) further improved the classification accuracy compared to models that did not consider this genomic information (AUROC: 0.847 ± 0.01 vs. 0.787 ± 0.03, p=0.01, AP: 0.68 ± 0.02 vs. 0.64 ± 0.05).

## Critical Analysis

The study provides valuable insights into the relationship between the genomic and imaging characteristics of CRC subtypes. By incorporating genomic data into the CNN models, the researchers were able to improve the accuracy of CRC subtype classification from histopathological images.

However, the study is limited to a specific dataset and may not be generalizable to all CRC cases. Additionally, the researchers only examined two types of genomic variations (CIMP and SNPs) and there may be other genetic factors that influence the cellular morphology patterns.

Further research is needed to [explore more explainable AI techniques](https://aimodels.fyi/papers/arxiv/exploring-explainable-ai-techniques-improved-interpretability-lung) that can provide a deeper understanding of the underlying mechanisms linking genomic variations to imaging phenotypes in CRC. This could lead to more accurate and clinically relevant subtype classification, ultimately guiding personalized treatment decisions for CRC patients.

## Conclusion

This study demonstrates the potential of incorporating genomic data into CNN models for improved [CRC subtype classification from histopathological images](https://aimodels.fyi/papers/arxiv/histopathological-image-classification-cell-morphology-aware-deep). By exploring the interplay between genomic variants and cellular morphology patterns, the researchers were able to enhance the accuracy of CRC subtype identification, which is crucial for guiding personalized treatment decisions. Further research in this direction could lead to more robust and interpretable AI-powered tools for cancer diagnosis and management.

Yosef E. Maruvka

Authored papers

Can AI learn to read a slide like a pathologist, not just spot the obvious hotspots?

Can AI learn to spot the hidden fingerprints of colorectal cancer subtypes in standard tissue scans, potentially guiding personalized treatment decisions?

Can AI learn to spot cancer patterns across different patient groups without getting confused by their differences?

Can AI reveal how a colorectal cancer cell's appearance betrays its hidden genetic mutations?

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