Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning

Perkonigg, Matthias; Hofmanninger, Johannes; Menze, Bjoern; Weber, Marc‐André

doi:10.1007/978-3-030-00807-9_3

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…[5] It also causes erosion of bones called bone lesions diagnosed in CT scans. [6] Approximately 1500 people who died due to this disease contribute to 0.2% of the total deaths caused by cancer alone in 2019 [7]. There are nearly 20,000 people diagnosed with myeloma every year in the US.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

et al. 2020

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Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

et al. 2020

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“…U-Nets have shown great potential in segmenting 2D microscopic images (Ronneberger et al, 2015), in detecting brain lesions (Kamnitsas et al, 2016) or bone lesions. In (Perkonigg et al, 2018) transfer learning is used to classify bone lesions in CT scans of MM patients. Our work was inspired by (Christ et al, 2017), where a cascade of two fully convolutional networks showed promising results for segmenting liver lesions in CT, by dividing the detection task in liver extraction and lesion detection within the liver region.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma

Licandro,

Hofmanninger,

Perkonigg

et al. 2019

Preprint

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The reliable and timely stratification of bone lesion evolution risk in smoldering Multiple Myeloma plays an important role in identifying prime markers of the disease's advance and in improving the patients' outcome. In this work we provide an asymmetric cascade network for the longitudinal prediction of future bone lesions for T1 weighted whole body MR images. The proposed cascaded architecture, consisting of two distinct configured U-Nets, first detects the bone regions and subsequently predicts lesions within bones in a patch based way. The algorithm provides a full volumetric risk score map for the identification of early signatures of emerging lesions and for visualising high risk locations. The prediction accuracy is evaluated on a longitudinal dataset of 63 multiple myeloma patients.

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Utilizing MobileNetV2 for Enhanced Blood Cancer Detection

Middha

2023

2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)

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Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning

Cited by 4 publications

References 7 publications

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

Asymmetric Cascade Networks for Focal Bone Lesion Prediction in Multiple Myeloma

Utilizing MobileNetV2 for Enhanced Blood Cancer Detection

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