Sébastien Quetin

Sébastien Quetin, Tapotosh Ghosh, Farhad Maleki

Contrastive learning methods in self-supervised settings have primarily focused on pre-training encoders, while decoders are typically introduced and trained separately for downstream dense prediction tasks. However, this conventional approach overlooks the potential benefits of jointly pre-training both encoder and decoder. In this paper, we propose DeCon, an efficient encoder-decoder self-supervised learning (SSL) framework that supports joint contrastive pre-training. We first extend existing SSL architectures to accommodate diverse decoders and their corresponding contrastive losses. Then, we introduce a weighted encoder-decoder contrastive loss with non-competing objectives to enable the joint pre-training of encoder-decoder architectures. By adapting an established contrastive SSL framework for dense prediction tasks, DeCon achieves new state-of-the-art results: on COCO object detection and instance segmentation when pre-trained on COCO dataset; across almost all dense downstream benchmark tasks when pre-trained on COCO+ and ImageNet-1K. Our results demonstrate that joint pre-training enhances the representation power of the encoder and improves performance in dense prediction tasks. This gain persists across heterogeneous decoder architectures, various encoder architectures, and in out-of-domain limited-data scenarios.

Sébastien Quetin, Andrew Heschl, Mauricio Murillo et al.

Purpose: To present a high-performing, robust, and flexible deep learning pipeline for automatic segmentation of 30 organs-at-risk (OARs) in head and neck (H&N) cancer patients, using MRI, CT, or both. Method: We trained a segmentation pipeline on paired CT and MRI-T1 scans from 296 patients. We combined data from the H&N OARs CT and MR segmentation (HaN-Seg) challenge and the Burdenko and GLIS-RT datasets from the Cancer Imaging Archive (TCIA). MRI was rigidly registered to CT, and both were stacked as input to an nnU-Net pipeline. Left and right OARs were merged into single classes during training and separated at inference time based on anatomical position. Modality Dropout was applied during the training, ensuring the model would learn from both modalities and robustly handle missing modalities during inference. The trained model was evaluated on the HaN-Seg test set and three TCIA datasets. Predictions were also compared with Limbus AI software. Dice Score (DS) and Hausdorff Distance (HD) were used as evaluation metrics. Results: The pipeline achieved state-of-the-art performance on the HaN-Seg challenge with a mean DS of 78.12% and HD of 3.42 mm. On TCIA datasets, the model maintained strong agreement with Limbus AI software (DS: 77.43% , HD: 3.27 mm), while also flagging low-quality contours. The pipeline can segment seamlessly from the CT, the MRI scan, or both. Conclusion: The proposed pipeline achieved the best DS and HD scores among all HaN-Seg challenge participants and establishes a new state-of-the-art for fully automated, multi-modal segmentation of H&N OARs.

Alyssa R. Amod, Alexandra Smith, Pearly Joubert et al.

A critical challenge for tumour segmentation models is the ability to adapt to diverse clinical settings, particularly when applied to poor-quality neuroimaging data. The uncertainty surrounding this adaptation stems from the lack of representative datasets, leaving top-performing models without exposure to common artifacts found in MRI data throughout Sub-Saharan Africa (SSA). We replicated a framework that secured the 2nd position in the 2022 BraTS competition to investigate the impact of dataset composition on model performance and pursued four distinct approaches through training a model with: 1) BraTS-Africa data only (train_SSA, N=60), 2) BraTS-Adult Glioma data only (train_GLI, N=1251), 3) both datasets together (train_ALL, N=1311), and 4) through further training the train_GLI model with BraTS-Africa data (train_ftSSA). Notably, training on a smaller low-quality dataset alone (train_SSA) yielded subpar results, and training on a larger high-quality dataset alone (train_GLI) struggled to delineate oedematous tissue in the low-quality validation set. The most promising approach (train_ftSSA) involved pre-training a model on high-quality neuroimages and then fine-tuning it on the smaller, low-quality dataset. This approach outperformed the others, ranking second in the MICCAI BraTS Africa global challenge external testing phase. These findings underscore the significance of larger sample sizes and broad exposure to data in improving segmentation performance. Furthermore, we demonstrated that there is potential for improving such models by fine-tuning them with a wider range of data locally.