1. Introduction

2. Research Methodology

3. Interpreting Model Design and Workflow

4. Deep Learning Models in Medical Imaging

5. Interpretation and Visualization Techniques

5.1. Perturbation-Based Methods

Perturbation-based methods evaluate how input changes affect model outputs to determine feature importance. By altering specific image regions, these methods identify areas that significantly influence predictions, typically visualized with heatmaps. Techniques like Integrated Gradients (IG), Local Interpretable Model-agnostic Explanations (LIME), and Occlusion Sensitivity (OS) are used in various domains, including breast cancer detection, eye disease classification, and brain MRI analysis (see Table 2). The table categorizes studies by domain, task, modality, performance, and technique. These methods also test model sensitivity to input variations, ensuring robust interpretations.

5.1.1. Occlusion

Zeiler and Fergus [40] introduced an occlusion method to assess the impact on model output when parts of an image are obstructed. Kermany et al. [41] utilized this method for interpreting optical coherence tomography images to diagnose retinal pathologies. A major limitation of occlusion is its high computational demand, as it requires inference for each occluded image region, increasing with image resolution and desired heatmaps.

5.1.2. Local Interpretable Model-Agnostic Explanations (LIME)

Ribeiro et al. [42] introduced local interpretable model-agnostic explanations (LIME) to identify superpixels (groups of connected pixels with similar intensities). Seah et al. [43] applied LIME to identify congestive heart failure in chest radiographs. LIME offers an advantage over occlusion by preserving the altered image portions’ context, as they are not completely blocked as shown in Figure 3.

5.1.3. Integrated Gradients

Sundararajan et al. [44] introduced integrated gradients (IG) to measure pixel importance by computing gradients across images interpolated between the original and a baseline image with all non-values. Sayres et al. [45] found that model-predicted grades and heatmaps improved the accuracy of diabetic retinopathy grading by readers.

Table 2. Overview of Various Studies Using Perturbation-Based Methods in Medical Imaging

Table 2. Overview of Various Studies Using Perturbation-Based Methods in Medical Imaging

Domain	Task	Modality	Performance	Technique	Citation
Breast	Classification	MRI	N/A	IG	[46]
Eye	Classification	DR	Accuracy: 95.5%	IG	[45]
Multiple	Classification	DR	N/A	IG	[44]
Chest	Detection	X-ray	Accuracy: 94.9%, AUC: 97.4%	LIME	[47]
Gastrointestinal	Classification	Endoscopy	Accuracy: 97.9%	LIME	[48]
Brain	Segmentation, Detection	MRI	ICC: 93.0%	OS	[49]
Brain	Classification	MRI	Accuracy: 85.0%	OS	[50]
Breast	Detection, Classification	Histology	Accuracy: 55.0%	OS	[51]
Eye, Chest	Classification, Detection	OCT, X-ray	Eye Accuracy: 94.7%, Chest Accuracy: 92.8%	OS	[41]
Chest	Classification	X-ray	AUC: 82.0%	OS, IG, LIME	[43]

5.2. Gradient-Based Methods

Backpropagation, used for weight adjustment in neural network training, is also employed in model interpretation methods to compute gradients. Unlike training, these methods do not alter weights but use gradients to highlight important image areas.

Figure 4. Examples of gradient-based attribution methods for model interpretability. (A) Class maximization visualization of malignant and benign breast masses on mammograms [9]. (B) Integrated gradients visualizing evidence of diabetic retinopathy on retinal fundus images [45]. (C) Visualization of malignant and benign breast masses [9]. (D) Guided backpropagation applied to ultrasound images for fetal heartbeat localization [4]. (E) Differentiation between benign and malignant breast masses in mammograms [6]. (F) Grad-CAM visualizations identifying discriminative regions in magnetoencephalography images for detecting eye-blink artifacts [52].

Figure 4. Examples of gradient-based attribution methods for model interpretability. (A) Class maximization visualization of malignant and benign breast masses on mammograms [9]. (B) Integrated gradients visualizing evidence of diabetic retinopathy on retinal fundus images [45]. (C) Visualization of malignant and benign breast masses [9]. (D) Guided backpropagation applied to ultrasound images for fetal heartbeat localization [4]. (E) Differentiation between benign and malignant breast masses in mammograms [6]. (F) Grad-CAM visualizations identifying discriminative regions in magnetoencephalography images for detecting eye-blink artifacts [52].

5.2.1. Saliency Maps

Saliency maps, introduced by Simonyan et al. [53], use gradient information to explain how deep convolutional networks classify images. They are used in class maximization and image-specific class saliency maps. Class maximization generates an image maximizing activation for a class, as in:

$$argmax{S}_c\left(I\right)-\lambda {\parallel I\parallel }_2^2$$

Yi et al. [9] applied this to visualize malignant and benign breast masses. Image-specific class saliency maps create heatmaps showing each pixel’s significance in classification, computed as:

$${\mathrm{Sal}}_c\left(x\right)={\displaystyle \frac{\partial F_c\left(x\right)}{\partial x}}$$

Dubost et al. [54] used these maps in a weakly supervised method for segmenting brain MRI structures. Saliency maps have also been utilized in diagnosing heart diseases in chest x-rays [8], classifying breast masses in mammography [55] with accuracy ranging from 85% to 92.9%, and identifying pediatric elbow fractures in x-rays [56] with accuracy of 88.0% and area under curve (AUC) of 95.0%. Moreover, iterative saliency maps [57] enhance less obvious image regions by generating a saliency map, inpainting prominent areas, and iterating the process until the image classification changes or a limit is reached. This approach, applied to retinal fundus images for diabetic retinopathy grading, demonstrated higher sensitivity compared to traditional saliency maps. However, saliency maps have limitations. They do not distinguish if a pixel supports or contradicts a class, and their effectiveness diminishes in binary classification.s.

5.2.2. Guided Backpropagation

Guided backpropagation, introduced by Springenberg et al. [58], builds on the saliency map approach by Simonyan et al. [53] and the deconvnet concept by Zeiler and Fergus [40]. It improves gradient backpropagation through ReLU layers, where negative activations are set to zero during the forward pass. Guided backpropagation discards gradients where either the forward activation or the backward gradient is negative, producing heatmaps that highlight pixels positively contributing to the classification.

In 2017, Gao and Noble [4] applied guided backpropagation to ultrasound images for fetal heartbeat localization. They found that the heatmaps remained consistent despite variations in the heart’s appearance, size, position, and contrast. Conversely, Böhle et al. [59] discovered that guided backpropagation was less effective for visualizing Alzheimer’s disease in brain MRIs compared to other methods. Similarly, Dubost et al. [60] achieved an intraclass correlation coefficient (ICC) of 93.0% in brain MRI detection using guided backpropagation. Wang et al. [61] obtained an average accuracy of 93.7% in brain MRI classification with this technique. Gessert et al. [62] reported an accuracy of 99.0% in cardiovascular classification using OCT images. Wickstrøm et al. [63] achieved a 94.9% accuracy in gastrointestinal segmentation using endoscopy. Lastly, Jamaludin et al. [64] reported an accuracy of 82.5% in musculoskeletal spine classification using MRI images with guided backpropagation.

5.2.3. Class Activation Maps (CAM)

Class Activation Mapping (CAM), introduced by Zhou et al. [65], visualizes regions of an image most influential in a neural network’s classification decision. CAM is computed as a weighted sum of feature maps from the final convolutional layer, using weights from the fully connected layer following global average pooling [66]. For a specific class c and image x:

$${\mathrm{CAM}}_c\left(x\right)=\sum _k{w}_k^c{f}_k\left(x\right)$$

This heatmap highlights regions most relevant for classification. CAM has been applied in various medical imaging applications, such as segmenting lung nodules in thoracic CT scans [67] and differentiating between benign and malignant breast masses in mammograms [6]. However, CAM’s effectiveness depends on the network architecture, requiring a global pooling (GAP) layer followed by a fully connected layer. While Zhou et al. [68] originally used GAP, Oquab et al. [69] demonstrated that global max pooling and log-sum-exponential pooling can also be used, with the latter yielding finer localization.

Table 3. Performance metrics of various Medical Imaging tasks across different modalities using CAM

Table 3. Performance metrics of various Medical Imaging tasks across different modalities using CAM

Domain-Task	Modality	Performance	Citation
Bladder Classification	Histology	Mean Accuracy: 69.9%	[70]
Brain Classification	MRI	Accuracy: 86.7%	[71,72]
Brain Detection	MRI,PET,CT	Accuracy: 90.2% - 95.3%, F1: 91.6% - 94.3%	[73,74]
Breast Classification	X-ray,Ultrasound, MRI	Accuracy: 83.0% -89.0%	[75,76,77,78]
Breast Detection	X-ray, Ultrasound	Mean AUC: 81.0%, AUC: Mt-Net 98.0%, Sn-Net 92.8%, Accuracy: 92.5%	[79,80,81,82]
Chest Classification	X-ray,CT	Accuracy: 97.8% , Average AUC: 75.5%- 96.0%	[83,84,85,86,87,88,89,90]
Chest Segmentation	X-ray	Accuracy: 95.8%	[91]
Eye Classification	Fundus Photography,OCT,CT	F1-score: 95.0% , Precision: 93.0%, AUC: 88.0%- 99.0%	[92,93,94,95,96]
Eye Detection	Fundus Photography	Accuracy: 73.2% -99.1%, AUC: 99.0%	[97,98,99,100,101]
GI Classification	Endoscopy	Mean Accuracy: 93.2%	[102,103,104,105]
Liver Classification, Segmentation	Histology	Mean Accuracy: 87.5%	[106,107]
Musculoskeletal Classification	MRI, X-ray	Accuracy: 86.0%, AUC: 85.3%	[108,109]
Skin Classification, Segmentation	Dermatoscopy	Accuracy: 83.6%, F1-score: 82.7%	[110,111]
Skull Classification	X-ray	AUC: 88.0%- 93.0%	[112]
Thyroid Classification	Ultrasound	Accuracy: 87.3% ± 0.0007, AUC: 90.1% ± 0.0007	[113]
Lymph Node Classification, Detection	Histology	Accuracy: 91.9%, AUC: 97.0%	[114]
Various Classification	CT, MRI, Ultrasound, X-ray, Fundoscopy	F1-score: 98.0%, Model Accuracy: 98.0%	[115,116]

5.2.4. Grad-CAM

Grad-CAM, an extension of CAM by Selvaraju et al. [117], broadens its application to any network architecture and output, including image segmentation and captioning. It bypasses the global pooling layer and weights feature maps directly with gradients calculated via backpropagation from a target class. The gradients of the output for class c concerning feature maps $A^k$ are averaged globally, multiplied by $A^k$, and passed through a ReLU activation to discard negative values:

$${\mathrm{Grad}-\mathrm{CAM}}_c\left(x\right)=\mathrm{ReLU}\left(\sum _k\left({\displaystyle \frac{1}{Z}}\sum _i\sum _j{\displaystyle \frac{\partial y^c}{\partial A_{ij}^k}}\right)A^k\right)$$

Garg et al. employed grad-CAM visualizations to identify discriminative regions of magnetoencephalography images in the task of detecting eye-blink artifacts [52]. The authors found that the regions of the eye highlighted by grad-CAM are the same regions that human experts rely on. Table 4 summarizes the effectiveness of Grad-CAM across various medical imaging tasks, highlighting domains, tasks, modalities, and performance metrics.

5.3. Decomposition-Based Methods

Decomposition-based techniques for model interpretation focus on breaking down a model’s prediction into a heatmap showing each pixel’s contribution to the final decision. These techniques, such as LRP, have been widely applied across different domains.

Figure 5. Example uses of decomposition-based attribution methods for model interpretability to show the Layerwise relevance propagation for model interpretation in diagnosing multiple sclerosis on brain MRI (Eitel et al., 2019)[160]. The Attention U-Net for organ segmentation in abdominal CT scans [161] and the areas upon which the model correctly focuses its predictions on the test images in explainable Vision Transformers model [162].

Figure 5. Example uses of decomposition-based attribution methods for model interpretability to show the Layerwise relevance propagation for model interpretation in diagnosing multiple sclerosis on brain MRI (Eitel et al., 2019)[160]. The Attention U-Net for organ segmentation in abdominal CT scans [161] and the areas upon which the model correctly focuses its predictions on the test images in explainable Vision Transformers model [162].

5.3.1. Layer-Wise Relevance Propagation (LRP)

Layer-Wise Relevance Propagation (LRP), introduced by Bach et al. in 2015 [163], offers an alternative to gradient-based techniques like saliency mapping, guided backpropagation, and Grad-CAM. Instead of relying on gradients, LRP distributes the output of the final layer back through the network to calculate relevance scores for each neuron. This process is repeated recursively from the final layer to the input layer, generating a relevancy heatmap that can be overlaid on the input image. Further properties of LRP and details of its theoretical basis are given in (Montavon et al., 2017 [164]), and a comparison of LRP to other interpretation methods can be found in ([165,166,167]).

The relevance score $R_{i\leftarrow k}^{(l,l+1)}$ for a neuron i in layer l from a neuron k in layer $l+1$ is defined as:

$$R_{i\leftarrow k}^{(l,l+1)}=R_k^{(l+1)}{\displaystyle \frac{a_i{w}_{ik}}{{\sum }_h{R}_{h\leftarrow k}^{(l,l+1)}}}$$

The overall relevance score for neuron i in layer l is:

$$R_i^l=\sum _k{R}_{i\leftarrow k}^{(l,l+1)}$$

LRP has been applied in MI, such as diagnosing multiple sclerosis (MS) and Alzheimer’s disease (AD) using MRI scans. For MS, LRP heatmaps highlighted hyperintense lesions and affected brain areas [160] as shown in Figure 5, while for AD, they emphasized the hippocampal volume, a critical region for diagnosis [168]. LRP has been found to provide clearer distinctions compared to gradient-based methods and has been used in frameworks like DeepLight for linking brain regions with cognitive states [169].

6. Comparison of Different Interpretation Methods

7. Current Challenges and Future Directions

8. Conclusion

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