A Multiple Instance Learning Framework for Robust and Explainable Medical Diagnosis

Core Concepts

A multiple instance learning (MIL) framework can effectively select the most discriminative image patches to perform robust and explainable medical diagnosis, without compromising performance compared to standard CNN and ViT models.

Abstract

The paper proposes a multiple instance learning (MIL) framework that can be integrated into both convolutional neural network (CNN) and vision transformer (ViT) architectures for medical image classification tasks. The key idea is to force the model to use only a subset of the most relevant image patches to reach the final classification, mimicking the clinical practice where medical decisions are based on localized findings.
The authors evaluate their approach on two medical applications: skin cancer diagnosis using dermoscopy images and breast cancer diagnosis using mammography. The results show that using only a small subset of the patches does not compromise the diagnostic performance for in-domain data, compared to baseline approaches. However, the MIL models are more robust to shifts in patient demographics, while also providing more detailed explanations about which regions contributed to the decision.
The paper first describes the patch encoder block, which can be either a CNN or a ViT, to extract patch-level representations from the input image. Then, the MIL block aggregates these patch features to predict the image-level classification. Two MIL approaches are explored: instance-level, which performs predictions on each patch, and embedding-level, which first aggregates the patch features before classification.
The experimental results demonstrate that the instance-level MIL models consistently outperform their embedding-level counterparts, suggesting that the key patches identified by the instance-level approach are more clinically relevant. Additionally, the MIL models achieve comparable or better performance than the baseline CNN and ViT models, while using significantly less information (i.e., a smaller subset of patches). This highlights the potential of MIL to create more efficient, explainable, and fair medical image analysis systems.
The paper also includes visualizations of the key patches identified by the MIL models, which align well with the clinically relevant regions, further validating the approach. Overall, the work establishes MIL as a promising method to improve the robustness and interpretability of medical image analysis.

Stats

The ISIC 2019 dataset contains 20,228 training images and 5,066 validation images for the binary classification task, and 1,270 test images for the multi-class classification task.
The DDSM dataset contains 2,428 training cases with findings and 1,342 training cases without findings, as well as 260 validation cases with findings and 137 validation cases without findings.
The HIBA dataset contains 200 test images for the multi-class classification task.
The PH2 dataset contains 827 test images for the binary classification task.
The Derm7pt dataset contains 827 test images for the binary classification task.

Quotes

"By forcing the model to use only a part of the image to perform a diagnosis, we can: i) improve its robustness to bias, as the information that the model can use is limited and thus it must select the most discriminative one; and ii) identify spurious correlations learned by the model (e.g., one or more ROIs matching artifacts instead of clinical findings)."
"Surprisingly, we also observed that, by using MIL, we obtain diagnostic systems that generalize better to new datasets, with different distributions and characteristics than those used for model training."

Key Insights Distilled From

Key Patches Are All You Need: A Multiple Instance Learning Framework For Robust Medical Diagnosis

by Diog... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01654.pdf

Deeper Inquiries

How can the MIL framework be extended to incorporate additional clinical information, such as patient demographics or medical history, to further improve the robustness and fairness of the diagnostic models

To incorporate additional clinical information into the MIL framework for improved robustness and fairness of diagnostic models, one approach is to integrate patient demographics and medical history as features in the input data. This can be achieved by preprocessing the data to include relevant patient information alongside the medical images. By including demographic variables such as age, gender, ethnicity, and medical history such as previous diagnoses, treatments, and comorbidities, the model can learn to consider these factors in its decision-making process.
Furthermore, the MIL framework can be extended to include attention mechanisms that focus on specific regions of the image based on the patient's demographic information. For example, the model can learn to prioritize certain patches or regions of interest based on the patient's age or medical history, allowing for more personalized and targeted diagnosis. By incorporating this additional clinical information, the model can improve its generalization capabilities across diverse patient populations and enhance the fairness of the diagnostic process.

What are the potential limitations of the MIL approach, and how can it be combined with other techniques, such as attention mechanisms or self-supervised learning, to address these limitations

While the MIL framework offers advantages in selecting key patches for diagnosis and improving model robustness, it also has potential limitations. One limitation is the reliance on predefined patch selection strategies, which may not always capture all relevant information in the image. To address this limitation, the MIL approach can be combined with attention mechanisms to dynamically focus on different regions of the image based on their importance for the diagnosis. This adaptive attention mechanism can enhance the model's ability to identify critical features and improve diagnostic accuracy.
Additionally, incorporating self-supervised learning techniques can help address the limitations of the MIL framework by enabling the model to learn more robust and generalizable representations from unlabeled data. Self-supervised learning tasks, such as image inpainting or rotation prediction, can provide additional context and information to the model, leading to better feature extraction and classification performance. By combining MIL with attention mechanisms and self-supervised learning, the model can overcome limitations related to patch selection and enhance its interpretability and performance.

Given the promising results on medical image analysis, how can the MIL framework be applied to other healthcare domains, such as electronic health records or medical text, to develop more comprehensive and interpretable clinical decision support systems

The MIL framework's success in medical image analysis can be extended to other healthcare domains, such as electronic health records (EHR) or medical text, to develop comprehensive clinical decision support systems. In the context of EHR data, the MIL framework can be applied to extract key information from patient records, such as lab results, medication history, and clinical notes, to assist in diagnosis and treatment planning. By treating each patient record as a "bag" of instances, the model can identify relevant patterns and associations for more accurate decision-making.
In medical text analysis, the MIL framework can be used to analyze clinical notes, research articles, or patient reports to extract key insights and support clinical decision-making. By identifying discriminative regions or key phrases in the text data, the model can provide explanations for its predictions and assist healthcare professionals in interpreting complex medical information. Additionally, the MIL framework can be combined with natural language processing techniques to enhance the understanding and utilization of textual data in healthcare applications.