We focus on the problem of Gallbladder Cancer (GBC) detection from Ultrasound (US) images. The problem presents unique challenges to modern Deep Neural Network (DNN) techniques due to low image quality arising from noise, textures, and viewpoint variations. Tackling such challenges would necessitate precise localization performance by the DNN to identify the discerning features for the downstream malignancy prediction. While several techniques have been proposed in the recent years for the problem, all of these methods employ complex custom architectures. Inspired by the success of foundational models for natural image tasks, along with the use of adapters to fine-tune such models for the custom tasks, we investigate the merit of one such design, ViT-Adapter, for the GBC detection problem. We observe that ViT-Adapter relies pre-dominantly on a primitive CNN-based spatial prior module to inject the localization information via cross-attention, which is inefficient for our problem due to the small pathology sizes, and variability in their appearances due to non-regular structure of the malignancy. In response, we propose, LQ-Adapter, a modified Adapter design for ViT, which improves localization information by leveraging learnable content queries over the basic spatial prior module. Our method surpasses existing approaches, enhancing the mean IoU (mIoU) scores by 5.4%, 5.8%, and 2.7% over ViT-Adapters, DINO, and FocalNet-DINO, respectively on the US image-based GBC detection dataset, and establishing a new state-of-the-art (SOTA). Additionally, we validate the applicability and effectiveness of LQ-Adapter on the Kvasir-Seg dataset for polyp detection from colonoscopy images. Superior performance of our design on this problem as well showcases its capability to handle diverse medical imaging tasks across different datasets. Source code and trained models are publicly released.

In recent years, automated Gallbladder Cancer (GBC) detection has gained the attention of researchers. Current state-of-the-art (SOTA) methodologies relying on ultra-sound sonography (US) images exhibit limited generalization, emphasizing the need for transformative approaches. We observe that individual US frames may lack sufficient information to capture disease manifestation. This study advocates for a paradigm shift towards video-based GBC detection, leveraging the inherent advantages of spatiotemporal representations. Employing the Masked Autoencoder (MAE) for representation learning, we address shortcomings in conventional image-based methods. We propose a novel design called FocusMAE to systematically bias the selection of masking tokens from high-information regions, fostering a more refined representation of malignancy. Additionally, we contribute the most extensive US video dataset for GBC detection. We also note that, this is the first study on US video-based GBC detection. We validate the proposed methods on the curated dataset, and report a new SOTA accuracy of 96.4% for the GBC detection problem, against an accuracy of 84% by current Image-based SOTA – GBCNet and RadFormer, and 94.7% by Video-based SOTA – AdaMAE. We further demonstrate the generality of the proposed FocusMAE on a public CT-based Covid detection dataset, reporting an improvement in accuracy by 3.3% over current baselines. Project page with source code, trained models, and data is available at: https://gbc-iitd.github.io/focusmae.

One common constraint in the practical application of speech recognition is Code Switching. The issue of code-switched languages is especially aggravated in the context of Indian languages – since most massively multilingual models are trained on corpora that are not representative of the diverse set of Indian languages. An associated constraint with such systems is the privacy-intrusive nature of the applications that aim to collate such representative data. To collectively mitigate both problems, this work presents CodeFed: A federated learning-based code-switching detection model that can be deployed to collaboratively be trained by leveraging private data from multiple users, without compromising their privacy. Using a representative low-resource Indic dataset, we demonstrate the superior performance of a collaboratively trained global model that is trained using federated learning on three low-resource Indic languages – Gujarati, Tamil and Telugu and draw a comparison of the model with respect to the most current work in the field. Finally, to evaluate the practical realizability of the proposed system, CodeFed also discusses the system overview of the label generation architecture which may accompany CodeFed’s possible real-time deployment.