SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms.

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing highdimensional microimage data.

Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm.

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes.

Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases.

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients' blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis.

Recurrent neural network from adder's perspective: Carry-lookahead RNN.

The recurrent network architecture is a widely used model in sequence modeling, but its serial dependency hinders the computation parallelization, which makes the operation inefficient. The same problem was encountered in serial adder at the early stage of digital electronics. In this paper, we discuss the similarities between recurrent neural network (RNN) and serial adder.

MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer.

Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example, the high distinction among people, size of bladder variation and noise interference. Based on the above issues, we propose an MD-Unet network structure, which uses multi-scale images as the input of the network, and combines max-pooling with dilated convolution to increase the receptive field of the convolutional network.

Active 3-D Shape Cosegmentation With Graph Convolutional Networks.

We present a novel active learning approach for shape cosegmentation based on graph convolutional networks (GCNs). The premise of our approach is to represent the collections of three-dimensional shapes as graph-structured data, where each node in the graph corresponds to a primitive patch of an oversegmented shape, and is associated with a representation initialized by extracting features. Then, the GCN operates directly on the graph to update the representation of each node based on a layer-wise propagation rule, which aggregates information from its neighbors, and predicts the labels for unlabeled nodes.

EEG classification of driver mental states by deep learning.

Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R.

Fine-grained leukocyte classification with deep residual learning for microscopic images.

Background And Objective: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories.

Methods: Based on deep learning theory, a systematic study is conducted on finer leukocyte classification in this paper.

A miR-135b-TAZ positive feedback loop promotes epithelial-mesenchymal transition (EMT) and tumorigenesis in osteosarcoma.

Transcriptional co-activator with PDZ-binding motif (TAZ) is a WW domain-containing protein that regulates mesenchymal differentiation and organ development. It is also a downstream effector of the Hippo signaling pathway, which has been implicated in epithelial-mesenchymal transition (EMT) and tumorigenesis. However, the molecular mechanisms underlying TAZ function in these processes in the context of osteosarcoma (OS) are not well understood.