Scientists at MIT and Stanford have unveiled a promising new way to help the immune system recognize and attack cancer cells more effectively. Their strategy targets a hidden “off switch” that tumors ...
First, we pretrained the encoder of a transformer-based network using a self-supervised approach on unlabeled abdominal computed tomography images. Subsequently, we fine-tuned the segmentation network ...
As shown below, the inferred masks predicted by our segmentation model trained by the dataset appear similar to the ground truth masks. This repository contains a curated and enhanced version of brain ...
Tumor segmentation in lung CT using U-Net, U-Net++ and an augmentation-enhanced U-Net. Best validation Dice: 0.807 (MSD lung dataset).
Retrospective analysis of ctDNA results from real-world data was performed for 61 patients (233 plasma time points) diagnosed with early-stage UC. ctDNA status and dynamics were assessed using a ...
ABSTRACT: Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving ...
ABSTRACT: Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving ...
Abstract: Medical image segmentation is a critical task in clinical diagnosis and treatment, particularly for brain tumor analysis using imaging modalities such as Magnetic Resonance Imaging (MRI) and ...
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