OTCE: Hybrid SSM and Attention with Cross Domain Mixture of Experts to construct Observer-Thinker-Conceiver-Expresser

Recent research has shown that combining Mamba with Transformer architecture, which has selective state space and quadratic self-attention mechanism, outperforms using Mamba or Transformer architecture alone in language modeling tasks. The quadratic self-attention mechanism effectively alleviates the shortcomings of selective state space in handling long-term dependencies of any element in the sequence. We propose a position information injection method that connects the selective state space model with the quadratic attention, and integrates these two architectures with hybrid experts with cross-sharing domains, so that we can enjoy the advantages of both. We design a new architecture with a more biomimetic idea: Observer-Thinker-Conceiver-Expresser (OTCE), which can compete with well-known medium-scale open-source language models on a small scale in language modeling tasks.

Multi-scale super-resolution generation of low-resolution scanned pathological images

Digital pathology slide is easy to store and manage, convenient to browse and transmit. However, because of the high-resolution scan for example 40 times magnification(40X) during the digitization, the file size of each whole slide image exceeds 1Gigabyte, which eventually leads to huge storage capacity and very slow network transmission. We design a strategy to scan slides with low resolution (5X) and a super-resolution method is proposed to restore the image details when in diagnosis. The method is based on a multi-scale generative adversarial network, which sequentially generate three high-resolution images such as 10X, 20X and 40X. The perceived loss, generator loss of the generated images and real images are compared on three image resolutions, and a discriminator is used to evaluate the difference of highest-resolution generated image and real image. A dataset consisting of 100,000 pathological images from 10 types of human tissues is performed for training and testing the network. The generated images have high peak-signal-to-noise-ratio (PSNR) and structural-similarity-index (SSIM). The PSNR of 10X to 40X image are 24.16, 22.27 and 20.44, and the SSIM are 0.845, 0.680 and 0.512, which are better than other super-resolution networks such as DBPN, ESPCN, RDN, EDSR and MDSR. Moreover, visual inspections show that the generated high-resolution images by our network have enough details for diagnosis, good color reproduction and close to real images, while other five networks are severely blurred, local deformation or miss important details. Moreover, no significant differences can be found on pathological diagnosis based on the generated and real images. The proposed multi-scale network can generate good high-resolution pathological images, and will provide a low-cost storage (about 15MB/image on 5X), faster image sharing method for digital pathology.