ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer

• URL: http://arxiv.org/abs/2501.01392v1
• Date: Thu, 02 Jan 2025 18:07:36 GMT
• Title: ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer
• Authors: Xuyin Qi, Zeyu Zhang, Aaron Berliano Handoko, Huazhan Zheng, Mingxi Chen, Ta Duc Huy, Vu Minh Hieu Phan, Lei Zhang, Linqi Cheng, Shiyu Jiang, Zhiwei Zhang, Zhibin Liao, Yang Zhao, Minh-Son To,
• Abstract summary: We propose ProjectedEx, a generative framework that provides interpretable, multi-attribute explanations.<n>Second, we enhance the encoder module by incorporating feature pyramids, which enables multiscale feedback to refine the latent space.<n>Third, we conduct comprehensive experiments on both the generator and classifier, demonstrating the clinical relevance and effectiveness of ProjectedEx.
• Score: 14.372695272204632
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Prostate cancer, a growing global health concern, necessitates precise diagnostic tools, with Magnetic Resonance Imaging (MRI) offering high-resolution soft tissue imaging that significantly enhances diagnostic accuracy. Recent advancements in explainable AI and representation learning have significantly improved prostate cancer diagnosis by enabling automated and precise lesion classification. However, existing explainable AI methods, particularly those based on frameworks like generative adversarial networks (GANs), are predominantly developed for natural image generation, and their application to medical imaging often leads to suboptimal performance due to the unique characteristics and complexity of medical image. To address these challenges, our paper introduces three key contributions. First, we propose ProjectedEx, a generative framework that provides interpretable, multi-attribute explanations, effectively linking medical image features to classifier decisions. Second, we enhance the encoder module by incorporating feature pyramids, which enables multiscale feedback to refine the latent space and improves the quality of generated explanations. Additionally, we conduct comprehensive experiments on both the generator and classifier, demonstrating the clinical relevance and effectiveness of ProjectedEx in enhancing interpretability and supporting the adoption of AI in medical settings. Code will be released at https://github.com/Richardqiyi/ProjectedEx

Related papers

• CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models [1.7042756021131187]
  This paper presents an automated radiology report generation framework that combines Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system. CBMs map chest X-ray features to human-understandable clinical concepts, enabling transparent disease classification. RAG system integrates multi-agent collaboration and external knowledge to produce contextually rich, evidence-based reports.
  arXiv  Detail & Related papers  (2025-04-29T16:14:55Z)
• Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework [5.211860566766601]
  The paper focuses on the integration of AI within a Human-Centric workflow to enhance breast cancer diagnostics. We propose a hybrid, multi-scale and multi-view Swin Transformer-based framework (MSMV-Swin) that enhances diagnostic robustness and accuracy.
  arXiv  Detail & Related papers  (2025-03-17T15:48:56Z)
• PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion [5.504796147401483]
  Development of reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbalances, and limited text annotations in datasets. We present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals.
  arXiv  Detail & Related papers  (2025-02-28T21:32:08Z)
• Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction [0.4915744683251151]
  This paper introduces ResUnet++, an advanced hybrid model combining ResNet and Unet++.<n>It is designed to improve tumour detection and localisation while fostering seamless interaction between clinicians and medical imaging systems.<n>By incorporating HCI principles, the model provides intuitive, real-time feedback, enabling clinicians to visualise and interact with tumour localisation results effectively.
  arXiv  Detail & Related papers  (2024-12-30T04:57:26Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  Synthetic Data Generation based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Applying Conditional Generative Adversarial Networks for Imaging Diagnosis [3.881664394416534]
  This study introduces an innovative application of Conditional Generative Adversarial Networks (C-GAN) integrated with Stacked Hourglass Networks (SHGN) We address the problem of overfitting, common in deep learning models applied to complex imaging datasets, by augmenting data through rotation and scaling. A hybrid loss function combining L1 and L2 reconstruction losses, enriched with adversarial training, is introduced to refine segmentation processes in intravascular ultrasound (IVUS) imaging.
  arXiv  Detail & Related papers  (2024-07-17T23:23:09Z)
• Synthetic Medical Imaging Generation with Generative Adversarial Networks For Plain Radiographs [34.98319691651471]
  The purpose of this investigation was to develop a reusable open-source synthetic image generation pipeline, the GAN Image Synthesis Tool (GIST) The pipeline helps to improve and standardize AI algorithms in the digital health space by generating high quality synthetic image data that is not linked to specific patients.
  arXiv  Detail & Related papers  (2024-03-28T02:51:33Z)
• Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis [17.4235794108467]
  The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes.
  arXiv  Detail & Related papers  (2024-03-26T09:55:49Z)
• Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation [48.08423125835335]
  MT-BI-RADS is a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. It offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy.
  arXiv  Detail & Related papers  (2023-08-27T22:07:42Z)
• Unsupervised Iterative U-Net with an Internal Guidance Layer for Vertebrae Contrast Enhancement in Chest X-Ray Images [1.521162809610347]
  We propose a novel and robust approach to improve the quality of X-ray images by iteratively training a deep neural network. Our framework includes an embedded internal guidance layer that enhances the fine structures of spinal vertebrae in chest X-ray images. Experimental results demonstrate that our proposed method surpasses existing detail enhancement methods in terms of BRISQUE scores.
  arXiv  Detail & Related papers  (2023-06-06T19:36:11Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.