Because of their state-of-the-art performance in computer vision, CNNs are becoming increasingly popular in a variety of fields, including medicine. However, as neural networks are black box function approximators, it is difficult, if not impossible, for a medical expert to reason about their output. This could potentially result in the expert distrusting the network when he or she does not agree with its output. In such a case, explaining why the CNN makes a certain decision becomes valuable information. In this paper, we try to open the black box of the CNN by inspecting and visualizing the learned feature maps, in the field of dermatology. We show that, to some extent, CNNs focus on features similar to those used by dermatologists to make a diagnosis. However, more research is required for fully explaining their output.
@cite_11: Background: The difficulties in accurately assessing pigmented skin lesions are ever present in practice. The recently described ABCD rule of dermatoscopy (skin surface microscopy at ×10 magnification), based on the criteria asymmetry (A), border (B), color (C), and differential structure (D), improved diagnostic accuracy when applied retrospectively to clinical slides. Objective: A study was designed to evaluate the prospective value of the ABCD rule of dermatoscopy in melanocytic lesions. Methods: In 172 melanocytic pigmented skin lesions, the criteria of the ABCD rule of dermatoscopy were analyzed with a semiquantitative scoring system before excision. Results: According to the retrospectively determined threshold, tumors with a score higher than 5.45 (64 69 melanomas [92.8 ]) were classified as malignant, whereas lesions with a lower score were considered as benign (93 103 melanocytic nevi [90.3 ]). Negative predictive value for melanoma (True-Negative ÷ [True-Negative + False-Negative]) was 9 5.8 , whereas positive predictive value (True-Positive ÷ [True-Positive + False-Positive]) was 85.3 . Diagnostic accuracy for melanoma (True-Positive ÷ [True-Positive + False- Positive + False-Negative]) was 80.0 , compared with 64.4 by the naked eye. Melanoma showed a mean final dermatoscopy score of 6.79 (SD, ± 0.92), significantly differing from melanocytic nevi (mean score, 4.27 ± 0.99; p U test). Conclusion: The ABCD rule can be easily learned and rapidly calculated, and has proven to be reliable. It should be routinely applied to all equivocal pigmented skin lesions to reach a more objective and reproducible diagnosis and to obtain this assessment preoperatively.
Concluding, typical visualization techniques either generate a single output image, in case of the feature visualization and the generation of the class representative, or function at the pixel level of the input image, in case of the region occlusion and the image-specific class saliency visualization. However, dermatologists typically scan a lesion for the presence of different individual features, such as asymmetry, border, color and structures, i.e. the so-called ABCD-score @cite_11 . Therefore, we inspect and visualize the intermediary feature maps of the CNN on a per-image basis, aiming to provide more familiar insights to dermatologists.