CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases.

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability.

The role of molecular biomarkers in the diagnosis, prognosis, and treatment stratification of oral squamous cell carcinoma: A comprehensive review.

One of the most common cancers targeting the area of the head and neck is oral squamous cell carcinoma (OSCC), carrying a heavy global health cost. With a high incidence of metastasis and recurrence, the outlook for OSCC remains dismal despite advancements in treatment. This has sparked an investigation into molecular biomarkers, which have the potential to improve early diagnosis, forecast patient outcomes, and direct therapeutic approaches.

IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT.

In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT).

Clinicopathological Analysis and Its Correlation With Various Classes of Lupus Nephritis.

Background and objectives Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by chronic immune complex deposition and involvement of multiple organ systems. Among individuals with SLE, a greater percentage are at a higher risk of developing lupus nephritis (LN). Renal biopsies play a pivotal role in diagnosing, managing, and predicting the prognosis of LN.