Explainable Reverse Verification of Goodness of Classification of MRI Images by Clinical Experts
Abstract:
Radiology offers a presumptive diagnosis. The etiology of radiological errors are prevalent, recurrent, and multi-factorial. The pseudo-diagnostic conclusions can arise from varying factors such as, poor technique, failures of visual perception, lack of knowledge, and misjudgments. This retrospective and interpretive errors can influence and alter the Ground Truth (GT) of Magnetic Resonance (MR) imaging which in turn result in faulty class labeling. Wrong class labels can lead to erroneous training and illogical classification outcomes for Computer Aided Diagnosis (CAD) systems. This work aims at verifying and authenticating the accuracy and exactness of the GT of biomedical datasets which are extensively used in binary classification frameworks. Generally such datasets are labeled by only one radiologist. Our article adheres a hypothetical approach to generate few faulty iterations. An iteration here considers simulation of faulty radiologist's perspective in MR image labeling. To achieve this, we try to simulate radiologists who are subjected to human error while taking decision regarding the class labels. In this context, we swap the class labels randomly and force them to be faulty. The experiments are carried out on some iterations (with varying number of brain images) randomly created from the brain MR datasets. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate our work, average classification parameter values of faulty iterations are compared with that of original dataset. It is presumed that, the presented approach provides a potential solution to verify the genuineness and reliability of the GT of the MR datasets. This approach can be utilized as a standard technique to validate the correctness of any biomedical dataset.
Insights
This study simulates radiologist errors in Magnetic Resonance (MR) image labeling to verify the accuracy of ground truth data. Validating data integrity is crucial for reliable Computer Aided Diagnosis (CAD) systems.
Area of Science:
- Medical Imaging Analysis
- Artificial Intelligence in Healthcare
- Radiology Informatics
Background:
- Radiological errors, stemming from factors like poor technique or misjudgment, can lead to inaccurate ground truth (GT) in Magnetic Resonance (MR) imaging.
- Faulty class labels in biomedical datasets compromise the training and performance of Computer Aided Diagnosis (CAD) systems.
- Current datasets often rely on single-radiologist labeling, increasing the risk of inherent biases and errors.
Purpose of the Study:
- To develop and validate a method for verifying the accuracy and reliability of ground truth (GT) in biomedical datasets, specifically MR imaging.
- To simulate the impact of human error in radiological interpretation on dataset labeling for binary classification tasks.
- To enhance the robustness of CAD systems by ensuring the integrity of training data.
Main Methods:
- A hypothetical approach was employed to simulate faulty radiologist perspectives by randomly swapping class labels in MR image datasets.
- Experiments were conducted on benchmark datasets (DS-75, DS-160) and a self-collected dataset (NITR-DHH) using simulated faulty iterations.
- The accuracy of the simulated faulty iterations was assessed by comparing average classification parameters against the original, correctly labeled dataset.
Main Results:
- Simulated faulty iterations demonstrated the potential impact of labeling errors on dataset integrity.
- Comparison of classification parameters between original and faulty datasets provides a quantitative measure of GT reliability.
- The study successfully generated and analyzed datasets with induced labeling errors.
Conclusions:
- The proposed approach offers a robust method for verifying the genuineness and reliability of ground truth in MR imaging datasets.
- This technique can serve as a standard validation tool for the correctness of any biomedical dataset used in machine learning.
- Ensuring data integrity is paramount for the development of trustworthy and effective Computer Aided Diagnosis (CAD) systems.
Related Concept Videos
01:24Magnetic Resonance Imaging
01:30Radiological Investigation II: MRI and Ventilation Perfusion Scan
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
01:21Imaging Studies for Cardiovascular System IV: CMRI
01:27Imaging Studies IV: Magnetic Resonance Imaging