Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas: A Pilot Study

Abstract

Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo . The aim of this prospective pilot study was to use a convolutional neural network classifier in hyperspectral images for differential diagnosis between pigmented basal cell carcinomas and melanoma. A total of 26 pigmented lesions (10 pigmented basal cell carcinomas, 12 melanomas in situ , 4 invasive melanomas) were imaged with hyperspectral imaging and excised for histopathological diagnosis. For 2-class classifier (melanocytic tumours vs pigmented basal cell carcinomas) using the majority of the pixels to predict the class of the whole lesion, the results showed a sensitivity of 100% (95% confidence interval 81–100%), specificity of 90% (95% confidence interval 60–98%) and positive predictive value of 94% (95% confidence interval 73–99%). These results indicate that a convolutional neural network classifier can differentiate melanocytic tumours from pigmented basal cell carcinomas in hyperspectral images. Further studies are warranted in order to confirm these preliminary results, using larger samples and multiple tumour types, including all types of melanocytic lesions.

Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images

“…In this paper, we presented a new unsupervised hierarchical partitioning method adapted to large-size datasets, such as hyperspectral aerial imagery. This method has eight main advantages that can be objectively listed: (1) no a priori knowledge is required, especially the non-introduction of training samples, to give the user the possibility to detect and locate known classes and new classes called "discovery classes"; (2) stability of the results thanks to its deterministic character; (3) selection of the exemplar of each class and the assignment of a pixel (or an object) to a class are done in a very elaborate way according to optimization criteria; (4) very low computing time with block processing, in contrast to the compared methods; (5) applicable to data or images whatever their size with the possibility of parallelizing the block partitioning; (6) possibility of elaborating several hierarchical partitions by indicating the most relevant one according to an objective criterion; (7) possibility of objectively selecting the samples of the classes in a learning system in order to be able to detect them afterwards; finally, (8) applicable to several domains without learning constraints. However, it requires more memory space.…”

“…Calculating all availabilities given the responsibilities according to Equations (3), (4), and (6) 4. Combining availabilities and responsibilities according to Equation (7) for each object x i and identifying exemplars x k that maximize [r(x i , x k ) + â(x i , x k )] 5. if exemplars do not change, proceeding to the next step (6) else repeat steps (2) to (4) until convergence end if 6. Merging every object to its nearest exemplar and break Output: Partition P of K classes and exemplar of each class…”

“…Due to this richness of information, which allows for better discrimination of objects, interest in hyperspectral images has increased during the last years in many application fields. These fields include geology [1], medicine [2][3][4][5][6][7], industrial production [8,9], safety [10], and the environment [11][12][13][14][15][16][17][18][19][20]. In this latter field, hyperspectral imagery has been of great interest and several applications have been treated.…”

Applications of Multispectral and Hyperspectral Imaging in Dermatology

“…For example, Räsänen et al leveraged deep learning convolutional neural networks in combination with hyperspectral images to differentiate melanocytic tumours from pigmented basal cell carcinomas. 33 By doing so, the model As such the former aspect of this adversarial network is interesting as it allows data generation without modelling the probability density function. 34 These generated images can be compared with learned manifold to produce a scoring system based on fitness.…”

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

“…In the field of skin tumours, HSI has been used to successfully identify solar field cancerization [ 6 ]. HSI can also distinguish lentigo maligna from lentigo maligna melanoma [ 7 ], and pigmented basal cell carcinomas (BCCs) from malignant melanomas (MMs) [ 8 ], as well as identifying the borders of lentigo maligna [ 9 ] and BCCs [ 10 ]. HSI has been used successfully in melanoma screening [ 11 , 12 , 13 , 14 ] and the diagnosis of skin lesions [ 15 ].…”