Cataract surgery is the most common surgical procedure globally, with a disproportionately higher burden in developing countries. While automated surgical video analysis has been explored in general surgery, its application to ophthalmic procedures remains limited. Existing works primarily focus on Phaco cataract surgery, an expensive technique not accessible in regions where cataract treatment is most needed. In contrast, Manual Small-Incision Cataract Surgery (MSICS) is the preferred low-cost, faster alternative in high-volume settings and for challenging cases. However, no dataset exists for MSICS. To address this gap, we introduce Sankara-MSICS, the first comprehensive dataset containing 53 surgical videos annotated for 18 surgical phases and 3,527 frames with 13 surgical tools at the pixel level. We benchmark this dataset on state-of-the-art models and present ToolSeg, a novel framework that enhances tool segmentation by introducing a phase-conditional decoder and a simple yet effective semi-supervised setup leveraging pseudo-labels from foundation models. Our approach significantly improves segmentation performance, achieving a 23.77% to 38.10% increase in mean Dice scores, with a notable boost for tools that are less prevalent and small. Furthermore, we demonstrate that ToolSeg generalizes to other surgical settings, showcasing its effectiveness on the CaDIS dataset.

One of the biggest issues facing humanity in the twenty-first century is climate change, as shown by the increasing sea levels, melting glaciers, and frequent storms. Accurate temperature forecasting is crucial for understanding and mitigating its impacts. Cutting-edge data-driven models for temperature forecasting typically employ recurrent neural networks(CNNs), with certain models integrating attention mechanisms. However RNNs sequential processing limits parallelization, especially for longer sequences. In order to do this, we provide a brand-new method for temperature prediction that is based on the FocalNet Transformer architecture. By functioning in a multi-tensor format, the suggested Focal-modulation Attention Encoder (FATE) framework leverages the spatial and temporal nuances of meteorological data characteristics by integrating tensorized modulation. Comparative assessments against existing transformer encoder architectures, 3D CNNs, LSTM, and ConvLSTM demonstrate our model’s superior ability to capture nuanced patterns inherent in the data, particularly in the context of temperature prediction. We also introduce a new labeled dataset, Climate change Parameter dataset (CCPD), which encompasses 40 years of data from J&K region on seven key parameters that influence climate change, supporting further research in this area. Experiments on two real-world benchmark temperature datasets from weather stations in the USA, Canada, and Europe demonstrate accuracy improvements of 12%, 23%, and 28% respectively, compared to existing SOTA models. In addition, we achieved state-of-the-art results on our CCPD dataset with a 24% improvement. To understand FATE, we introduce two modulation scores from the tensorial modulation process. These scores clarify our model’s decision making and key climate change parameters. For reproducible research, we will release the source code, pre-trained FATE model, and CCPD dataset.

We focus on the problem of Unsupervised Domain Adaptation (UDA) for breast cancer detection from mammograms (BCDM) problem. Recent advancements have shown that masked image modeling serves as a robust pretext task for UDA. However, when applied to crossdomain BCDM, these techniques struggle with breast abnormalities such as masses, asymmetries, and micro-calcifications, in part due to the typically much smaller size of region of interest in comparison to natural images. This often results in more false positives per image (FPI) and significant noise in pseudo-labels typically used to bootstrap such techniques. Recognizing these challenges, we introduce a transformerbased Domain-invariant Mask Annealed Student Teacher autoencoder (D-MASTER) framework. D-MASTER adaptively masks and reconstructs multiscale feature maps, enhancing the model’s ability to capture reliable target domain features. D-MASTER also includes adaptive confidence refinement to filter pseudo-labels, ensuring only high-quality detections are considered. We also provide a bounding box annotated subset of 1000 mammograms from the RSNA Breast Screening Dataset (referred to as RSNA-BSD1K) to support further research in BCDM. We evaluate D-MASTER on multiple BCDM datasets acquired from diverse domains. Experimental results show a significant improvement of 9% and 13% in sensitivity at 0.3 FPI over state-of-the-art UDA techniques on publicly available benchmark INBreast and DDSM datasets respectively. We also report an improvement of 11% and 17% on In-house and RSNA-BSD1K datasets respectively. To promote reproducible research and address the scarcity of accessible resources in BCDM, we will publicly release source code, and pre-trained D-MASTER model, along with RSNA-BSD1K annotations.

In clinical applications, X-Ray technology plays a crucial role in noninvasive examinations like mammography, providing essential anatomical information about patients. However, the inherent radiation risk associated with X-Ray procedures raises significant concerns. X-Ray reconstruction is crucial in medical imaging for creating detailed visual representations of internal structures, and facilitating diagnosis and treatment without invasive procedures. Recent advancements in deep learning (DL) have shown promise in X-Ray reconstruction. Nevertheless, conventional DL methods often necessitate the centralized aggregation of substantial large datasets for training, following specific scanning protocols. This requirement results in notable domain shifts and privacy issues. To address these challenges, we introduce the Hierarchical Framework based Federated Learning method (HF-Fed) for customized X-Ray Imaging. HF-Fed addresses the challenges in X-Ray imaging optimization by decomposing the problem into two components: local data adaptation and holistic X-Ray Imaging. It employs a hospital-specific hierarchical framework and a shared common imaging network called Network of Networks (NoN) for these tasks. The emphasis of the NoN is on acquiring stable features from a variety of data distributions. A hierarchical hypernetwork extracts domain-specific hyperparameters, conditioning the NoN for customized X-Ray reconstruction. Experimental results demonstrate HF-Fed’s competitive performance, offering a promising solution for enhancing X-Ray imaging without the need for data sharing. This study significantly contributes to the evolving body of literature on the potential advantages of federated learning in the healthcare sector. It offers valuable insights for policymakers and healthcare providers holistically.

Federated learning has emerged as a promising paradigm for collaborative machine learning, enabling multiple clients to train a model while preserving data privacy jointly. Tailored federated learning takes this concept further by accommodating client heterogeneity and facilitating the learning of personalized models. While the utilization of transformers within federated learning has attracted significant interest, there remains a need to investigate the effects of federated learning algorithms on the latest focal modulation-based transformers. In this paper, we investigate this relationship and uncover the detrimental effects of federated averaging (FedAvg) algorithms on Focal Modulation, particularly in scenarios with heterogeneous data. To address this challenge, we propose TransFed, a novel transformer-based federated learning framework that not only aggregates model parameters but also learns tailored Focal Modulation for each client. Instead of employing a conventional customization mechanism that maintains client-specific focal modulation layers locally, we introduce a learn-to-tailor approach that fosters client collaboration, enhancing scalability and adaptation in TransFed. Our method incorporates a hyper network on the server, responsible for learning personalized projection matrices for the focal modulation layers. This enables the generation of client-specific keys, values, and queries. Furthermore, we provide an analysis of adaptation bounds for TransFed using the learn-to-customize mechanism. Through intensive experiments on datasets related to pneumonia classification, we demonstrate that TransFed, in combination with the learn-to-tailor approach, achieves superior performance in scenarios with non-IID data distributions, surpassing existing methods. Overall, TransFed paves the way for leveraging focal Modulation in federated learning, advancing the capabilities of focal modulated transformer models in decentralized environments.

Human pose estimation is the process of continuously monitoring a person's action and movement to track and monitor the activity of a person or an object. Human pose estimation is usually done by capturing the key points which describe the pose of a person. A guiding practicing framework that enables people to learn and exercise activities like yoga, fittness, dancing, etc., might be built using human posture recognition remotely and accurately without the help of a personal trainer. This work has proposed a framework to detect and recognize various yoga and exercise poses to help the individual practice the same correctly. A popular Blaze-pose model extracts key points from the student end and compares the same with the instructor pose. The extracted key points are fed to the Human Pose Juxtaposition model (HPJT) to compare the student pose with the instructor. The model will assess the correctness of the pose by comparing the extracted key points and give feedback to students if any corrections need to be made. The proposed model is trained with 40+ yoga and exercise poses, and evaluated the model's performance with the mAP, and the model achieved an accuracy of 99.04%. The results proved that any person could use the proposed framework in real-time to practice exercise, yoga, dance, etc. At their respective location without the help of a physical instructor with precision and accuracy, leading to a healthy life.

In this paper, we present a Climate Change Parameter Dataset (CCPD) intending to achieve state-of-the-art results in parameters which effect climate change, including forest cover, water bodies, agriculture and vegetation, population, temperature, construction, and air index. The dataset can be used by the research community to validate the claims made in relation to the climate change. Research community has been deeply involved in extending the use case of machine learning algorithms to the effects of climate change. However, the non-availability of sufficient data related to climate change parameters has limited the research in this domain. By presenting this dataset, we want to facilitate the researchers. In this dataset, we provide a large variety of statistical and satellite data acquired by various image processing techniques and on-ground data collection. The data is collected in abundance for a specific region, and then various machine learning techniques are used to extract the useful data related to each parameter separately. We call this amalgam of processed data as CCPD dataset. CCPD dataset contains over 6000 data points for all seven parameters and covers the data from 1960 onwards. We hope this dataset will aid the research community in tackling climate change with the help of AI.

The area of Computer Vision has gone through exponential growth and advancement over the past decade. It is mainly due to the introduction of effective deep-learning methodologies and the availability of massive data. This has resulted in the incorporation of intelligent computer vision schemes to automate the different number of tasks. In this paper, we have worked on similar lines. We have proposed an integrated system for the development of robotic arms, considering the current situation in fruit identification, classification, counting, and generating their masks through semantic segmentation. The current method of manually doing these processes is time-consuming and is not feasible for large fields. Due to this, multiple works have been proposed to automate harvesting tasks to minimize the overall overhead. However, there is a lack of an integrated system that can automate all these processes together. As a result, we are proposing one such approach based on different machine learning techniques. For each process, we propose to use the most effective learning technique with computer vision capability. Thus, proposing an integrated intelligent end-to-end computer vision-based system to detect, classify, count, and identify the apples. In this system, we modified the YOLOv3 algorithm to detect and count the apples effectively. The proposed scheme works even under variable lighting conditions. The system was trained and tested using a standard benchmark i.e., MinneApple. Experimental results show an average accuracy of 91%..