The project

  • Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning: [GitHub]
  • Histopathological transfer learning for Acute Lymphoblastic Leukemia detection: [GitHub]

Computer Aided Diagnosis (CAD) systems are increasingly utilizing image analysis and Deep Learning (DL) techniques, due to their high accuracy in several medical imaging fields, including the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) from peripheral blood samples. However, no method in the literature has specifically analyzed the focus quality of ALL images or proposed a technique for sharpening the samples in an adaptive way for the purpose of classification.

Another issue of CAD systems is that, when using DL, the limited dimensionality of ALL databases favors the use of transfer learning techniques to increase the accuracy in the detection, by considering Convolutional Neural Networks (CNN) pretrained on the general purpose ImageNet database. However, no method in the literature has yet considered the use of CNNs pretrained on histopathology databases to perform transfer learning for ALL detection. In fact, the majority of histopathology databases in the literature has either a small number of samples or limited ground truth labeling possibilities (e.g., only two possible classes), which hinders the effectiveness of training CNNs from scratch.

The method

Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning

We propose the first machine learning-based approach able to enhance blood sample images by an adaptive unsharpening method. The method uses image processing techniques and DL to normalize the radius of the cell, estimate the focus quality, adaptively improve the sharpness of the images, and then perform the classification. We evaluated the methodology on a public database of ALL images, considering several state-of-the-art CNNs to perform the classification, with results showing the validity of the proposed approach.

Histopathological transfer learning for Acute Lymphoblastic Leukemia detection

We propose the first method based on histopathological transfer learning for ALL detection, which trains a CNN on a histopathology database to classify tissue types, then performs a fine tuning on the ALL database to detect the presence of lymphoblasts. As histopathology database, we consider a multi-label dataset with a significantly higher number of samples and classes with respect to the literature, which enables CNNs to learn general features for histopathology image processing and hence allow to perform a more effective transfer learning, with respect to CNNs pretrained on ImageNet. We evaluate the methodology on a publicly-available ALL database and considering multiple CNNs, with results confirming the validity of our approach.

Related publications

  • A. Genovese, M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti, "Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning", in Proc. of the 2021 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, ON, Canada, June 6-11, 2021, pp. 1205-1209. ISBN: 978-1-7281-7605-5. [DOI: 10.1109/ICASSP39728.2021.9414362] [PDF] [BibTex entry]
  • A. Genovese, M. S. Hosseini, V. Piuri, K. N. Plataniotis, and F. Scotti, "Histopathological transfer learning for Acute Lymphoblastic Leukemia detection", in Proc. of the 2021 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2021), June 18-20, 2021, pp. 1-6. ISBN: 978-1-6654-1249-0. [DOI: 10.1109/CIVEMSA52099.2021.9493677] [PDF] [BibTex entry]

Citations

@InProceedings {icassp21,
    author = {A. Genovese and M. S. Hosseini and V. Piuri and K. N. Plataniotis and F. Scotti},
    booktitle = {Proc. of the 2021 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2021)},
    title = {Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning},
    address = {Toronto, ON, Canada},
    pages = {1205-1209},
    month = {June},
    day = {6-11},
    year = {2021},
    note = {978-1-7281-7605-5},
}

@InProceedings {civemsa21all,
    author = {A. Genovese and M. S. Hosseini and V. Piuri and K. N. Plataniotis and F. Scotti},
    booktitle = {Proc. of the 2021 IEEE Int. Conf. on Computational Intelligence and Virtual Environments for
    Measurement Systems and Applications (CIVEMSA 2021)},
    title = {Histopathological transfer learning for Acute Lymphoblastic Leukemia detection},
    pages = {1-6},
    month = {June},
    day = {18-20},
    year = {2021},
    note = {978-1-6654-1249-0},
}


Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research, within the project "Deep Learning and CUDA for advanced and less-constrained biometric systems".



People



Downloads

  • Acute Lymphoblastic Leukemia detection based on adaptive unsharpening and Deep Learning: [GitHub]
  • Histopathological transfer learning for Acute Lymphoblastic Leukemia detection: [GitHub]
  • Acute Lymphoblastic Leukemia Image Database for Image Processing: ALL-IDB