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Found 5 papers, 1 papers with code
Breast Cancer Image Classification Method Based on Deep Transfer Learning
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed.
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example
To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection.
Survival Prediction Across Diverse Cancer Types Using Neural Networks
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
RITFIS: Robust input testing framework for LLMs-based intelligent software
To our knowledge, RITFIS is the first framework designed to assess the robustness of LLM-based intelligent software against natural language inputs.
LEAP: Efficient and Automated Test Method for NLP Software
The widespread adoption of DNNs in NLP software has highlighted the need for robustness.
