Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis. Recent advancements in artificial intelligence have paved the way for the application of deep learning algorithms to complex medical tasks. Nevertheless, there are many challenges in applying effective learning algorithms to medical image analysis, such as the lack of sufficient and reliably annotated training datasets and the highly class-imbalanced nature of most medical data. Here, we improve on the state-of-the-art methodologies of bone marrow cell recognition by deviating from sole reliance on labeled data and leveraging self-supervision in training our learning models. We investigate our approach's effectiveness in identifying bone marrow cell types. Our experiments demonstrate significant performance improvements in conducting different bone marrow cell recognition tasks compared to the current state-of-the-art methodologies.
PaperView - going over the paper -> "Li Z, Chen Y, LeCun Y, Sommer FT. Neural Manifold Clustering and Embedding. arXiv preprint arXiv:2201.10000. 2022 Jan 24."
PaperView - going over the paper Zhu, Jinguo, et al. "Complementary relation contrastive distillation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains. These methods rely on domain-specific augmentations that are not directly amenable to the tabular domain. Instead, we introduce Contrastive Mixup, a semi-supervised learning framework for tabular data and demonstrate its effectiveness in limited annotated data settings. Our proposed method leverages Mixup-based augmentation under the manifold assumption by mapping samples to a low dimensional latent space and encourage interpolated samples to have high a similarity within the same labeled class. Unlabeled samples are additionally employed via a transductive label propagation method to further enrich the set of similar and dissimilar pairs that can be used in the contrastive loss term. We demonstrate the effectiveness of the proposed framework on public tabular datasets and real-world clinical datasets.
PaperView - going over the paper titled "TabNN - A Universal Neural Network Solution for Tabular Data" by G Ke, J Zhang, Z Xu, J Bian, TY Liu
PaperView - going over the paper titled "Transferable Adversarial Training - A General Approach to Adapting Deep Classifiers" by Hong Liu, Mingsheng Long,Jianmin Wang, and Michael I. Jordan