2022-08-20 更新
CCRL: Contrastive Cell Representation Learning
Authors:Ramin Nakhli, Amirali Darbandsari, Hossein Farahani, Ali Bashashati
Cell identification within the H&E slides is an essential prerequisite that can pave the way towards further pathology analyses including tissue classification, cancer grading, and phenotype prediction. However, performing such a task using deep learning techniques requires a large cell-level annotated dataset. Although previous studies have investigated the performance of contrastive self-supervised methods in tissue classification, the utility of this class of algorithms in cell identification and clustering is still unknown. In this work, we investigated the utility of Self-Supervised Learning (SSL) in cell clustering by proposing the Contrastive Cell Representation Learning (CCRL) model. Through comprehensive comparisons, we show that this model can outperform all currently available cell clustering models by a large margin across two datasets from different tissue types. More interestingly, the results show that our proposed model worked well with a few number of cell categories while the utility of SSL models has been mainly shown in the context of natural image datasets with large numbers of classes (e.g., ImageNet). The unsupervised representation learning approach proposed in this research eliminates the time-consuming step of data annotation in cell classification tasks, which enables us to train our model on a much larger dataset compared to previous methods. Therefore, considering the promising outcome, this approach can open a new avenue to automatic cell representation learning.
PDF Accepted to ECCVW 2022
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CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation
Authors:Renhao Wang, Hang Zhao, Yang Gao
Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap exists largely because commonly used random crop augmentations obtain semantically inconsistent content in crowded scene images of diverse objects. Previous works use preprocessing pipelines to localize salient objects for improved cropping, but an end-to-end solution is still elusive. In this work, we propose a framework which accomplishes this goal via joint learning of representations and segmentation. We leverage segmentation masks to train a model with a mask-dependent contrastive loss, and use the partially trained model to bootstrap better masks. By iterating between these two components, we ground the contrastive updates in segmentation information, and simultaneously improve segmentation throughout pretraining. Experiments show our representations transfer robustly to downstream tasks in classification, detection and segmentation.
PDF Accepted to ECCV 2022
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Contrastive Learning for Object Detection
Authors:Rishab Balasubramanian, Kunal Rathore
Contrastive learning is commonly used as a method of self-supervised learning with the “anchor” and “positive” being two random augmentations of a given input image, and the “negative” is the set of all other images. However, the requirement of large batch sizes and memory banks has made it difficult and slow to train. This has motivated the rise of Supervised Contrasative approaches that overcome these problems by using annotated data. We look to further improve supervised contrastive learning by ranking classes based on their similarity, and observe the impact of human bias (in the form of ranking) on the learned representations. We feel this is an important question to address, as learning good feature embeddings has been a long sought after problem in computer vision.
PDF arXiv admin note: substantial text overlap with arXiv:2208.06083
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Contrastive Learning for OOD in Object detection
Authors:Rishab Balasubramanian, Rupashree Dey, Kunal Rathore
Contrastive learning is commonly applied to self-supervised learning, and has been shown to outperform traditional approaches such as the triplet loss and N-pair loss. However, the requirement of large batch sizes and memory banks has made it difficult and slow to train. Recently, Supervised Contrasative approaches have been developed to overcome these problems. They focus more on learning a good representation for each class individually, or between a cluster of classes. In this work we attempt to rank classes based on similarity using a user-defined ranking, to learn an efficient representation between all classes. We observe how incorporating human bias into the learning process could improve learning representations in the parameter space. We show that our results are comparable to Supervised Contrastive Learning for image classification and object detection, and discuss it’s shortcomings in OOD Detection
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