Abstract
Infrared (IR) spectroscopic imaging is of potentially wide use in medical imaging applications due to its ability to capture both chemical and spatial information. This complexity of the data both necessitates using machine intelligence as well as presents an opportunity to harness a high-dimensionality data set that offers far more information than today's manually-interpreted images. While convolutional neural networks (CNNs), including the well-known U-Net model, have demonstrated impressive performance in image segmentation, the inherent locality of convolution limits the effectiveness of these models for encoding IR data, resulting in suboptimal performance. In this work, we propose an INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation (INSTRAS). This novel model leverages the strength of the transformer encoders to segment IR breast images effectively. Incorporating skip-connection and transformer encoders, INSTRAS overcomes the issue of pure convolution models, such as the difficulty of capturing long-range dependencies. To evaluate the performance of our model and existing convolutional models, we conducted training on various encoder-decoder models using a breast dataset of IR images. INSTRAS, utilizing 9 spectral bands for segmentation, achieved a remarkable AUC score of 0.9788, underscoring its superior capabilities compared to purely convolutional models. These experimental results attest to INSTRAS's advanced and improved segmentation abilities for IR imaging.