Learning better representations is essential in medical image analysis for computer-aided diagnosis. However, learning discriminative semantic features is a major challenge due to the lack of large-scale well-annotated datasets. Thus, how can we learn a well-structured categorizable embedding space in limited-scale and unlabeled datasets? In this paper, we proposed a novel clustering-guided twin-contrastive learning framework (CTCL) that learns the discriminative representations of probe-based confocal laser endomicroscopy (pCLE) images for gastrointestinal (GI) tumor classification. Compared with traditional contrastive learning, in which only two randomly augmented views of the same instance are considered, the proposed CTCL aligns more semantically related and class-consistent samples by clustering, which improved intra-class tightness and inter-class variability to produce more informative representations. Furthermore, based on the inherent properties of CLE (geometric invariance and intrinsic noise), we proposed to regard CLE images with any angle rotation and CLE images with different noises as the same instance, respectively, for increased variability and diversity of samples. By optimizing CTCL in an end-to-end expectation-maximization framework, comprehensive experimental results demonstrated that CTCL-based visual representations achieved competitive performance on each downstream task as well as more robustness and transferability compared with existing state-of-the-art SSL and supervised methods. Notably, CTCL achieved 75.60%/78.45% and 64.12%/77.37% top-1 accuracy on the linear evaluation protocol and few-shot classification downstream tasks, respectively, which outperformed the previous best results by 1.27%/1.63% and 0.5%/3%, respectively. The proposed method holds great potential to assist pathologists in achieving an automated, fast, and high-precision diagnosis of GI tumors and accurately determining different stages of tumor development based on CLE images.

The connection study between biological structure and function, as well as between anatomical data and mechanical or physiological models, has been of increasing significance with the rapid advancement in experimental physiology and computational physiology. The China Physiome Project (CPP) is dedicated in optimization of the connection exploration based on standardization and integration of the structural datasets and their derivatives of cryosectional images with various standards, collaboration mechanisms, and online services. The CPP framework hereby incorporates the three-dimensional anatomical models of human and rat anatomy, the finite-element models of whole-body human skeleton, and the multiparticle radiological dosimetry data of both the human and rat computational phantoms. The ontology of CPP was defined using MeSH and, with its all standardized models description implemented by M3L, a multiscale modeling language based on XML. Provided services based on Wiki concept include collaboration research, modeling version control, data sharing, online analysis of M3L documents. As a sample case, a multiscale model for human heart modeling, in which familiar hypertrophic cardiomyopathy was studied according to the structure-function relations from genetic level to organ level, is integrated into the framework and given for demonstration of the functionality of multiscale physiological modeling based on CPP.

Computational models for small experimental animals are important in medical imaging and radiation dosimetry researches. The boundary representation method by use of nonuniform rational B-splines (NURBS) is adopted in this paper to develop a rat computational phantom based on the previously obtained segmentation data for cryosectional color photographic images of an adult male Sprague-Dawley rat. Continuous two-dimensional contours for a total of 14 major structures were outlined from the downscaled anatomical atlas, corresponding to a voxel size of 0.2 times 0.2 times 0.2 mm3and stacked to reconstruct the three-dimensional (3-D) shapes. The NURBS model was then appropriately fitted through the surface of each organ. Monte Carlo simulation of cone beam X-ray computed tomography was performed focusing on the thorax region to demonstrate the usefulness of the computational phantom in radiological imaging. The integrated whole-body geometry is presented with smooth internal and exterior boundaries. Organ centroid coordinates and volume information are tabulated for future comparison purposes. The rat phantom can be used in 3-D dose calculation and other computational applications as well.