Self-supervised learning-based Multi-Scale feature Fusion Network for survival analysis from whole slide images

Review Oncology

Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review

Lucas Schneider et al.

Summary: This systematic review discusses the multimodal fusion methods of convolutional neural network-based image analysis with omics data for cancer research, focusing on the impact of data combination on classification performance.

EUROPEAN JOURNAL OF CANCER (2022)

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Article Computer Science, Interdisciplinary Applications

Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

Weihang Dai et al.

Summary: This paper proposes AdaCon, a contrastive learning framework for deep image regression, which incorporates a novel adaptive-margin contrastive loss and a regression prediction branch for feature learning. By considering label distance relationships in feature representations, AdaCon achieves better performance in downstream regression tasks. Experimental results on two medical image regression tasks demonstrate the effectiveness of AdaCon, with relative improvements of 3.3% and 5.9% in MAE compared to state-of-the-art methods for BMD estimation and LVEF prediction, respectively.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Masked Autoencoders Are Scalable Vision Learners

Kaiming He et al.

Summary: This paper presents a self-supervised learning method for computer vision based on masked autoencoders. By masking a portion of the input image and reconstructing the missing pixels, large models can be trained efficiently and effectively. The approach achieves high generalization performance and outperforms supervised pretraining in transfer learning tasks.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

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Article Computer Science, Interdisciplinary Applications

Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From Whole-Slide Pathological Images

Wei Shao et al.

Summary: WSI is considered as the gold standard for cancer diagnosis and prognosis. However, existing prediction models do not efficiently utilize ordinal information among patients, and the large size and single label of WSIs complicate the training process.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

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Review Biochemistry & Molecular Biology

Deep learning in histopathology: the path to the clinic

Jeroen van der Laak et al.

Summary: Recent advancements in machine learning have shown great potential to enhance medical diagnostics, particularly in the field of histopathology. However, challenges remain in implementing these techniques in clinical settings.

NATURE MEDICINE (2021)

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Review Engineering, Multidisciplinary

A Survey on Contrastive Self-Supervised Learning

Ashish Jaiswal et al.

Summary: Self-supervised learning, particularly through contrastive learning, has gained popularity for its cost-effective approach in using self-defined pseudolabels for various downstream tasks. This paper extensively reviews self-supervised methods following the contrastive approach, explaining pretext tasks and different architectures used. Performance comparisons across multiple downstream tasks demonstrate variations in method effectiveness.

TECHNOLOGIES (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Big Self-Supervised Models Advance Medical Image Classification

Shekoofeh Azizi et al.

Summary: Self-supervised pretraining followed by supervised fine-tuning has been successful in medical image classification, significantly improving accuracy. The Multi-Instance Contrastive Learning method shows advantages in constructing informative positive pairs for self-supervised learning. Big self-supervised models are robust to distribution shift and can efficiently learn even with a small number of labeled medical images.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

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Article Computer Science, Artificial Intelligence