RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models

• URL: http://arxiv.org/abs/2401.08847v2
• Date: Wed, 3 Jul 2024 07:57:53 GMT
• Title: RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
• Authors: Farhad Maleki, Linda Moy, Reza Forghani, Tapotosh Ghosh, Katie Ovens, Steve Langer, Pouria Rouzrokh, Bardia Khosravi, Ali Ganjizadeh, Daniel Warren, Roxana Daneshjou, Mana Moassefi, Atlas Haddadi Avval, Susan Sotardi, Neil Tenenholtz, Felipe Kitamura, Timothy Kline,
• Abstract summary: This paper introduces the RIDGE checklist to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work.
• Score: 1.4675465116143782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption. This paper introduces the RIDGE checklist, a comprehensive framework designed to assess the Reproducibility, Integrity, Dependability, Generalizability, and Efficiency of deep learning-based medical image segmentation models. The RIDGE checklist is not just a tool for evaluation but also a guideline for researchers striving to improve the quality and transparency of their work. By adhering to the principles outlined in the RIDGE checklist, researchers can ensure that their developed segmentation models are robust, scientifically valid, and applicable in a clinical setting.

Related papers

• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• Adversarial Vessel-Unveiling Semi-Supervised Segmentation for Retinopathy of Prematurity Diagnosis [9.683492465191241]
  We propose a semi supervised segmentation framework designed to advance ROP studies without the need for extensive manual vessel annotation. Unlike previous methods that rely solely on limited labeled data, our approach integrates uncertainty weighted vessel unveiling module and domain adversarial learning. We validate our approach on public datasets and an in-house ROP dataset, demonstrating its superior performance across multiple evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-14T02:40:34Z)
• DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization [13.038800602897354]
  We develop an adversarial approach using a large language model to re-identify the patient corresponding to a redacted clinical note. Our method uses a large language model to reidentify the patient corresponding to a redacted clinical note. Although ClinicalBERT was the most effective, masking all identified PII, our tool still reidentified 9% of clinical notes.
  arXiv  Detail & Related papers  (2024-10-22T14:06:31Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• A Trustworthy Framework for Medical Image Analysis with Deep Learning [71.48204494889505]
  TRUDLMIA is a trustworthy deep learning framework for medical image analysis. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
  arXiv  Detail & Related papers  (2022-12-06T05:30:22Z)
• Demystifying Deep Learning Models for Retinal OCT Disease Classification using Explainable AI [0.6117371161379209]
  The adoption of various deep learning techniques is quite common as well as effective, and its statement is equally true when it comes to implementing it into the retina Optical Coherence Tomography sector. These techniques have the black box characteristics that prevent the medical professionals to completely trust the results generated from them. This paper proposes a self-developed CNN model which is comparatively smaller and simpler along with the use of Lime that introduces Explainable AI to the study.
  arXiv  Detail & Related papers  (2021-11-06T13:54:07Z)
• Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis [77.34726150561087]
  PAC-Bayes flatness-based and path norm-based measures produce the most consistent explanation for the combination of models and data. We also investigate the use of multi-task classification and segmentation approach for breast images.
  arXiv  Detail & Related papers  (2021-03-04T20:58:22Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• 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)
• Learning to Segment Anatomical Structures Accurately from One Exemplar [34.287877547953194]
  Methods that permit to produce accurate anatomical structure segmentation without using a large amount of fully annotated training images are highly desirable. We propose Contour Transformer Network (CTN), a one-shot anatomy segmentor including a naturally built-in human-in-the-loop mechanism. We demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning approaches.
  arXiv  Detail & Related papers  (2020-07-06T20:27:38Z)

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.