Research on imbalance machine learning methods for MR$$T_1$$WI soft tissue sarcoma data

Liu, Xuanxuan; Guo, Li; Wang, Hexiang; Guo, Jia; Duan, Lisha

doi:10.1186/s12880-022-00876-5

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…Addressing such imbalance is acknowledged as necessary in the training of surgical ML where complications occur in a minority of patients. 38 , 39 Alternative clinically applied data balancing strategies include over-sampling by duplicating the minority class to match the majority class (which is prone to overfitting) or supplementing the minority class with synthetic data. 40 …”

Section: Discussionmentioning

confidence: 99%

A feasibility study assessing quantitative indocyanine green angiographic predictors of reconstructive complications following nipple-sparing mastectomy

Dalli,

Nguyen,

Jindal

et al. 2024

JPRAS Open

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Section: Discussionmentioning

confidence: 99%

A feasibility study assessing quantitative indocyanine green angiographic predictors of reconstructive complications following nipple-sparing mastectomy

Dalli,

Nguyen,

Jindal

et al. 2024

JPRAS Open

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“…The investigation and application of ML to STS has lagged bone sarcoma because of a more heterogeneous appearance and fewer diagnostic characteristics. A significant proportion of the literature of ML in STS focuses on the use of radiomics for predicting tumor grade or biologic behavior 43-45 . Lee et al created an ensemble ML algorithm that used multiple sequence inputs comparisons (T1 + T2, T1 + T2 +contrast T1, T1 + T2 + diffusion weighted imaging, and all of the above), for differentiation of benign vs. soft tissue differentiation with AUC of 0.752, 0.756, 0.750, 0.749, respectively 46 .…”

Section: Applications Of Ai In Orthopaedic Oncologymentioning

confidence: 99%

Machine Learning–Assisted Decision Making in Orthopaedic Oncology

Rizk,

Gonzalez,

Galoaa

et al. 2024

JBJS Reviews

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» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

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“…However, one major challenge in developing effective deep-learning models for medical image analysis is the presence of a class imbalance in the datasets [1], [7]- [11]. Class imbalance in a medical image dataset refers to a situation where the number of images belonging to different classes or categories is significantly unequal [8]. Class imbalance occurs when specific categories of abnormalities in medical image datasets are prominent compared to the other classes.…”

Section: Introductionmentioning

confidence: 99%

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

Cui,

Li,

Yang

et al. 2024

IEEE Access

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Medical image datasets, particularly those comprising Magnetic Resonance (MR) images, are essential for accurate diagnosis and treatment planning. However, these datasets often suffer from class imbalance, where certain classes of abnormalities have unequal representation. Models trained on imbalanced datasets can be biased towards the prominent class, leading to misclassification. Addressing class imbalance problems is crucial to developing robust deep-learning MR image analysis models. This research focuses on the class imbalance problem in MR image datasets and proposes a novel approach to enhance deep learning models. We have introduced a unified approach equipped with a selective attention mechanism, unified loss function, and progressive resizing. The selective attention strategy identifies prominent regions within the underlying image to find the feature maps, retaining only the relevant activations of the minority class. Finetuning of the multiple hyperparameters was achieved using a novel unified loss function that plays a vital role in enhancing the overwhelming error performance for minority classes and accuracy for common classes. To address the class imbalances phenomenon, we incorporate progressive resizing that can dynamically adjust the input image size as the model trains. This dynamic nature helps handle class imbalances and improve overall performance. The research evaluates the proposed approach's effectiveness by embedding it into five state-of-the-art CNN models: UNet, FCN, RCNN, SegNet, and Deeplab-V3. For experimental purposes, we have selected five diverse MR image datasets, BUS2017, MICCAI 2015 head and neck, ATLAS, BRATS 2015, and Digital Database Thyroid Image (DDTI), to evaluate the performance of the proposed approach against state-of-the-art techniques. The assessment of the proposed approach reveals improved performance across all metrics for five different MR imaging datasets. DeepLab-V3 demonstrated the best performance, achieving IoU, DSC, Precision, and Recall scores of 0.893, 0.953, 0.943, and 0.944, respectively, on the BUS dataset. These scores indicate an improvement of 5% in DSC, 6% in IoU, 4% in precision, and approximately 4% in recall compared to the baseline. The most significant increases were observed in the ATLAS and LiTS MICCAI 2017 datasets, with a 5% and 7% increase in IoU and DSC over the baseline (DSC = 0.628, DSC = 0.695) for the ATLAS dataset, and a 5% and 9% increase in IoU and DSC for the LiTS MICCAI 2017 dataset.

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Research on imbalance machine learning methods for MR$$T_1$$WI soft tissue sarcoma data

Cited by 7 publications

References 40 publications

A feasibility study assessing quantitative indocyanine green angiographic predictors of reconstructive complications following nipple-sparing mastectomy

A feasibility study assessing quantitative indocyanine green angiographic predictors of reconstructive complications following nipple-sparing mastectomy

Machine Learning–Assisted Decision Making in Orthopaedic Oncology

A Unified Approach Addressing Class Imbalance in Magnetic Resonance Image for Deep Learning Models

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