Oncology/Hematology

Artificial intelligence model for perigastric blood vessel recognition during laparoscopic radical gastrectomy with D2 lymphadenectomy in locally advanced gastric cancer.

BACKGROUND: Radical gastrectomy with D2 lymphadenectomy is standard surgical protocol for locally advanced gastric cancer. The surgical experience and skill in recognizing blood vessels and performing lymph node dissection differ between surgeons, which may influence intraoperative safety and postoperative oncological outcomes. Hence, the aim of this study was to develop an accurate and real-time ...

Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment

Multimodal learning has been demonstrated to enhance performance across various clinical tasks, owing to the diverse perspectives offered by different modalities of data. However, existing multimodal segmentation methods rely on well-registered multimodal data, which is unrealistic for real-world clinical images, particularly for indistinct and diffuse regions such as liver tumors. In this paper...

Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly i...

Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation

This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumo...

Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain...

Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction

Background: Radiation pneumonitis is a side effect of thoracic radiation therapy. Recently, machine learning models with radiomic features have impr...

TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer im...

[Research progress on endoscopic image diagnosis of gastric tumors based on deep learning].

Gastric tumors are neoplastic lesions that occur in the stomach, posing a great threat to human health. Gastric cancer represents the malignant form o...

Text-Driven Tumor Synthesis

Tumor synthesis can generate examples that AI often misses or over-detects, improving AI performance by training on these challenging cases. However...

SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos

Vehicle detection and tracking in satellite video is essential in remote sensing (RS) applications. However, upon the statistical analysis of existi...

VisionLLM-based Multimodal Fusion Network for Glottic Carcinoma Early Detection

The early detection of glottic carcinoma is critical for improving patient outcomes, as it enables timely intervention, preserves vocal function, an...

Analysis of Transferred Pre-Trained Deep Convolution Neural Networks in Breast Masses Recognition

Breast cancer detection based on pre-trained convolution neural network (CNN) has gained much interest among other conventional computer-based syste...

MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification

Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizi...

Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models

This research presents an innovative approach to cancer diagnosis and prediction using explainable Artificial Intelligence (XAI) and deep learning t...

MatchMiner-AI: An Open-Source Solution for Cancer Clinical Trial Matching

Clinical trials drive improvements in cancer treatments and outcomes. However, most adults with cancer do not participate in trials, and trials ofte...

The Potential of Convolutional Neural Networks for Cancer Detection

Early detection is a prime requisite for successful cancer treatment and increasing its survivability rates, particularly in the most common forms. ...

PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network for Breast Ultrasound Images Segmentation

With the rapid development of deep learning and computer vision technologies, medical image segmentation plays a crucial role in the early diagnosis...

Diffusion-Based Approaches in Medical Image Generation and Analysis

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, off...

From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer

It is clinically crucial and potentially very beneficial to be able to analyze and model directly the spatial distributions of cells in histopatholo...

From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba

Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clini...