In this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set.

Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic.

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces.

In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery.

Background In the photodynamic therapy (PDT) of non-aggressive basal cell carcinomas (BCCs), 5-aminolevulinic acid nanoemulsion (BF-200ALA) has shown non-inferior efficacy when compared with methyl aminolevulinate (MAL), a widely used photosensitizer. Hexyl aminolevulinate (HAL) is an interesting alternative photosensitizer. To our knowledge, this is the first study using HAL-PDT in the treatment of BCCs. Objectives To compare the histological clearance, tolerability (pain and post-treatment reaction) and cosmetic outcome of MAL, BF-200 ALA and low-concentration HAL in the PDT of non-aggressive BCCs. Methods Ninety-eight histologically verified non-aggressive BCCs met the inclusion criteria, and 54 patients with 95 lesions completed the study. The lesions were randomized to receive LED-PDT in two repeated treatments with MAL, BF-200 ALA or HAL. Efficacy was assessed both clinically and confirmed histologically at three months by blinded observers. Furthermore, cosmetic outcome, pain, post-treatment reactions fluorescence and photobleaching were evaluated. Results According to intention-to-treat analyses, the histologically confirmed lesion clearance was 93.8% (95% confidence interval [CI] = 79.9-98.3) for MAL, 90.9% (95% CI = 76.4-96.9) for BF-200 ALA and 87.9% (95% CI = 72.7-95.2) for HAL, with no differences between the arms (P = 0.84). There were no differences between the arms as regards pain, post-treatment reactions or cosmetic outcome. Conclusions Photodynamic therapy with low-concentration HAL and BF-200 ALA has a similar efficacy, tolerability and cosmetic outcome compared to MAL. HAL is an interesting new option in dermatological PDT, since good efficacy is achieved with a low concentration.