Thus it is a lightweight architecture and also less prone to overfit. Besides, in the case of simple datasets, the pruned version of the CNL-UNet can be used. We evaluated our proposed architecture on multimodal biomedical image datasets, namely Chest X-ray, Dermoscopy, Microscopy, Ultrasound,...
multimodallearningandhowtoemploydeeparchitecturestolearnmultimodalrepresentations. Multimodallearninginvolvesrelatinginformationfrommultiplesources.Forexample,imagesand 3-ddepthscansarecorrelatedatfirst-orderasdepthdiscontinuitiesoftenmanifestasstrongedges inimages.Conversely,audioandvisualdataforspeechrecognitionhavenon-linea...
On the contrary, deep models can find the optimal set of features during training. In addition, deep models such as auto-encoders and CNNs can be used to perform unsupervised feature generation, and then to combine with a more sophisticated decision layer. This architecture enables the modeling...
In this work, we present 1D-CNN based multimodal deep learning architecture that use time-series features and medical Funding This study has been partially funded by The Scientific and Technological Research Council of Turkey (TUBITAK), Grant Number:120E173. Conflict of interest The authors declare...
VQA is a challenging task that requires a deep understanding of both computer vision and natural language processing. In recent years, VQA has seen significant progress due to the use of deep learning techniques and architectures, particularly the Transformer architecture. The Transformer architecture ...
This study aimed to develop a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. We designed the architecture of deep neural network that combined ResNet-50 with 3D CNNs for MR images and...
Transformer models: Models like ViLBERT or VisualBERT extend the transformer architecture to multimodal data, enabling more sophisticated understanding and generation tasks. End-to-end learning: Efforts to develop models that learn directly from raw multimodal data, reducing the reliance on pre-trained ...
Tran, D., Ray, J., Shou, Z., Chang, S.-F., Paluri, M.: ConvNet architecture search for spatiotemporal feature learning.arXiv:1708.05038(2017) Wang, L., Li, Y., Lazebnik, S.: Learning deep structure-preserving image-text embeddings. In: IEEE Conference on Computer Vision and Patt...
We used two separate computer vision-based approaches to fit the data. First, we used the ResNet50 architecture to fine-tune the multi-class classification algorithm using RBG frames. Second, we used the ResNet50 architecture with the same fine-tuning strategy against optica...
This one is "a bonus" to illustrate the use of multi-target losses, more than actually a different architecture.from pytorch_widedeep.preprocessing import TabPreprocessor, TextPreprocessor, ImagePreprocessor from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep, ModelFuser, Vision from ...