Abstract Library

#4586 Base on neuroendocrine tumour classification in CT imaging using pretrained vision transformers and data augmentation

Introduction: Neuroendocrine tumours (NETs) are a group of cancers that can be difficult to distinguish from other malignancies in CT imaging. Their diverse appearance often leads to diagnostic challenges, requiring advanced techniques to improve classification accuracy. Pretrained Vision Transformers (ViTs) have shown promise in medical image analysis, but additional methods are needed to enhance model robustness, particularly when dealing with complex and heterogeneous tumour characteristics.

Conference:

Presenting Author:

Authors: Li H, Tang Z, Ding Z, Chen Y, Li H,

Keywords: Vision transformers, Cancer Detection, Transfer Learning, Medical Image Analysis,

#4580 Improving diagnosis of neuroendocrine tumours using large language models

Introduction: Neuroendocrine tumours (NETs) are a heterogeneous group of cancers that can be difficult to diagnose due to their variable clinical presentation and non-specific symptoms. Accurate diagnosis often requires integrating a wide range of clinical, radiological, and pathological information. Large language models (LLMs) have shown promise in processing and understanding complex medical text, making them a valuable tool for improving diagnostic accuracy in oncology.

Conference:

Presenting Author:

Authors: Tang Z, Chen P, Tang J, Li H, Chen Y,

Keywords: Neuroendocrine Tumour (NET), Large Language Model (LLM), Medical Natural Language Processing (NLP), Clinical Decision Support,

#4554 Base on deep learning-integrated multimodal diagnostic framework for enhanced early detection of neuroendocrine tumours

Introduction: Neuroendocrine tumours (NETs) are a heterogeneous group of malignant neoplasms originating from neuroendocrine cells, commonly found in the digestive system, lungs, pancreas, and other organs. Due to their diverse clinical presentations and overlap with common diseases, the early diagnosis of NETs is challenging, often leading to misdiagnosis or delayed diagnosis at advanced stages.

Conference:

Presenting Author:

Authors: Ding Z, Li H, Tang Z, Chen Y, Li H,

Keywords: Neuroendocrine Tumour, Early Disease Screening, Multimodal Medical Data Analysis,

#4525 MRI findings of jejunal mesentery neuroendocrine tumour – A case report

Introduction: Neuroendocrine tumours arising from the mesentery are uncommon, with corresponding MRI features infrequently documented in the literature.

Conference:

Presenting Author:

Authors: Guo L, Li Z, Yang H, Dong H, Wang Y,

Keywords: Magnetic Resonance Imaging, Jejunal Mesentery, Neuroendocrine Tumour, Diagnosis,

#4522 Leveraging large language models for enhanced diagnosis of neuroendocrine tumours

Introduction: Neuroendocrine tumours (NETs) are a heterogeneous group of cancers that are difficult to diagnose due to their variable clinical presentation and non-specific symptoms. Accurate diagnosis often requires integrating a wide range of clinical, radiological, and pathological information. However, clinicians may struggle to synthesise these diverse data sources effectively. Large language models (LLMs) have shown promise in processing and understanding complex medical text, making them a valuable tool for improving diagnostic accuracy in oncology.

Conference:

Presenting Author:

Authors: Tang Z, Tang J, Cheng F, Li H, Chen Y,

Keywords: Neuroendocrine Tumour (NET), Large Language Model (LLM), Medical Natural Language Processing (NLP), Clinical Decision Support,