# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 4 Discussion  
The proposed method demonstrates superior performance compared to the baseline methods in various aspects, including its utilization of the YOLOX model, an anchor-free YOLO variant. Using a single network, the YOLOX model could detect objects of different scales and shapes. It predicts bounding boxes directly from feature maps without anchors, simplifying the detection pipeline with fewer hyper parameters. Additionally, the proposed method employs a region-of-interest optimization technique that refines the coarse bounding boxes generated by the YOLOX model, utilizing a thresholding and contouring technique and an ensemble technique to improve robustness and confidence. Furthermore, the proposed method could handle different types of mammograms and modalities, using the YOLOX model that could adapt to input images, and it could utilize  
Figure 7: Results of classification and detecting of breast cancer area. Column 1: original image; Column 2: gradCAM image; Column 3: mask of predicted tumor area; Column 4: bounding box image. existing convolutional neural network models trained for image classification without modification or fine-tuning, as it extracts relevant features for breast cancer detection using gradient-weighted class activation mapping. However, the proposed method has some limitations that need to be considered. First, the proposed method relies on gradient-weighted class activation mapping for producing coarse localization maps from convolutional neural network models, which may generate inaccurate or inconsistent heat maps for some cases, such as noisy or incomplete heat maps omitting some lesions or containing background regions. Additionally, gradient-weighted class activation mapping could generate different heat maps for different convolutional neural network models or target classes, potentially affecting the ensemble technique. Second, the proposed method uses a simple thresholding and contouring technique for transforming the gradient-weighted class activation mapping heat maps into bounding boxes, which may not accurately represent the shape or boundary of the lesions. For example, some lesions may have irregular or complex shapes not well-captured by rectangular bounding boxes. Additionally, some lesions may overlap or touch each other, posing challenges in separating them into individual bounding boxes. Lastly, the proposed method uses a fixed threshold of 0.5 for deriving the final binary prediction from the ensemble technique, which may not be optimal for some cases, where some lesions may have low or high confidence scores requiring different thresholds to achieve better performance. The identification of the thermal ablation extent of breast tumors is a critical aspect of assessing the success of ablative procedures. Previous research, such as the study by [PERSON] et al. (2020) [PERSON] et al. (2020), investigated the role of ablation margins near tumors. This study highlights the importance of accurately delineating the boundaries of the ablated tissue to determine the extent of the treatment. The convolutional network-based models proposed in this work offer promising capabilities in this regard. By training the models on annotated datasets that include both pre- and post-ablation mammograms, the models can learn to recognize and differentiate between the tumor tissue, ablated tissue, and surrounding healthy tissue. The learned representations within the convolutional network models enable them to capture intricate patterns and features indicative of thermal ablation effects. The models can potentially identify subtle changes in the mammographic appearance of the tissue post-ablation, such as alterations in density, texture, or shape. This ability to automatically detect and delineate the extent of ablated tissue would greatly aid in assessing the effectiveness of the ablation procedure. Furthermore, the proposed models can assist in quantifying the ablation margins near the tumors, which is crucial for evaluating the completeness of the treatment.