AIMC Topic: Image Interpretation, Computer-Assisted

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Optimizing Pix2Pix GAN With Attention Mechanisms for AI-Driven Polyp Segmentation in IoMT-Enabled Smart Healthcare.

IEEE journal of biomedical and health informatics
This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing p...

Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading.

BMC medical informatics and decision making
PURPOSE: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.

Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.

Scientific reports
Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet...

Deep learning network enhances imaging quality of low-b-value diffusion-weighted imaging and improves lesion detection in prostate cancer.

BMC cancer
BACKGROUND: Diffusion-weighted imaging with higher b-value improves detection rate for prostate cancer lesions. However, obtaining high b-value DWI requires more advanced hardware and software configuration. Here we use a novel deep learning network,...

FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.

Scientific reports
Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional imaging te...

An automated deep learning framework for brain tumor classification using MRI imagery.

Scientific reports
The precise and timely diagnosis of brain tumors is essential for accelerating patient recovery and preserving lives. Brain tumors exhibit a variety of sizes, shapes, and visual characteristics, requiring individualized treatment strategies for each ...

An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.

Scientific reports
Breast cancer represents a significant global health challenge, which makes it essential to detect breast cancer early and accurately to improve patient prognosis and reduce mortality rates. However, traditional diagnostic processes relying on manual...

Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.

BMC medical imaging
OBJECTIVE: Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of differe...

Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.

BMC medical imaging
BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, placing a significant burden on healthcare systems globally. Developing high-precision automated diagnostics requires large annotated datasets, which are costly and difficult to o...

Towards Skin Cancer Detection Through Low Resolution Images.

Studies in health technology and informatics
Currently, dermatologists need to check numerous image reports (high resolution) for diagnosing skin conditions, and Machine Learning (ML) models can help with this tedious task. However, current ML models usually work best with high-quality images i...