Who We Are

With 11 years of tradition, the LIAC Lab is committed to providing support for complementary diagnosis, performing clinical analysis tests with quality and safety for all its clients: physicians, patients, farming, and business organizations using artificial intelligence, machine learning, and various data analysis tools.

The LIAC Lab has a team of specialized professionals, state-of-the-art equipment, and information technology, where it is possible to provide high levels of technical, ethical, and moral service to its clients, according to the standards established by the Bangladeshi Society of Data Analysis. We are traditional in quality in the exams we perform, with high reliability between the medical and health classes of our state.

Explainable AI in Medical Image Analysis

Medical image analysis is important for diagnosing and treating diseases, especially in eye care and cancer treatment. This field uses advanced machine learning methods, especially convolutional neural networks (CNNs), to study medical images. However, these models can be complex and hard to understand, which is a problem in clinical settings where knowing how decisions are made is crucial. To address this, explainable AI techniques are developed to clarify how CNNs classify different eye conditions from retinal images. Additionally, new segmentation models are introduced to accurately identify blood vessels in these images, which helps doctors assess vascular health. By combining classification and segmentation, eye doctors can make better decisions and provide timely care, ultimately improving patient outcomes. Segmentation is vital in medical imaging as it helps separate images into important parts, allowing healthcare providers to focus on specific features for accurate diagnosis and treatment. For lung and colon cancer detection, explainable AI techniques also enhance understanding by showing how certain image features affect predictions, which helps build trust among healthcare professionals and improves communication with patients about their diagnoses.

Machine Translation and Regional Dialect Detection

Machine Translation (MT) is a part of natural language processing (NLP) that helps automatically translate text from one language to another. A major improvement in MT comes from Transformer models, which make translations faster and better. These models can read entire sentences at once, making them great for translating complex sentences. Text classification is another important NLP task that involves assigning predefined categories or labels to text data. However, many languages, especially low-resource languages, lack enough linguistic resources like annotated corpora and dictionaries to develop advanced NLP applications. This is often the case for languages spoken by marginalized communities. In Bangladesh, there are many regional dialects of Bangla that can differ greatly in vocabulary, pronunciation, and syntax. For instance, the daily conversational dialects in regions like Sylhet, Noakhali, and Mymensingh have unique expressions and phrases that are distinct from Standard Bangla. Dialect Machine Translation (DMT) aims to translate these regional dialects into Standard Bangla, the official language, but it faces challenges like the wide variability in dialects, which can create confusion in translation models. Additionally, there is often a scarcity of datasets specifically designed for dialect translations, making it hard to create strong models. Dialects also carry cultural meanings that may not always translate well, so it’s essential to handle local expressions carefully.

Natural Language Processing for Medical Question Answering

Developing a question-answering (QA) system using Natural Language Processing (NLP) in low-resource languages like Bangla, especially in the medical field, presents both challenges and opportunities. While the core objectives remain similar—processing and understanding medical literature, clinical notes, and patient records to accurately respond to queries—building such systems in low-resource settings requires adapting to the limitations of available data and resources. In the context of Bangla, the system must be capable of extracting relevant information from medical texts or generating meaningful responses, even though large datasets, pre-trained models, and medical resources in Bangla are scarce compared to high-resource languages. The system needs to handle the complexities of understanding medical terminology and relationships between medical concepts in Bangla, while also incorporating code-switching for English medical terms. It can provide clinical decision support to healthcare professionals, enabling them to make informed decisions, especially in rural areas, and offer patient education by answering questions in Bangla, thereby improving healthcare accessibility. The system would need to handle various question types such as factoid, confirmation, list, casual, temporal, and even unanswerable questions.

Generative Adversarial Networks in Agriculture

Generative Adversarial Networks (GANs) have revolutionized the field of machine learning, offering groundbreaking applications across various industries, particularly in agriculture. One significant area of focus is the detection of potato diseases, where gathering images of infected crops can be challenging due to limited access to diseased samples at different stages. GANs provide an innovative solution by generating synthetic data that mimics real-world conditions, significantly enhancing the ability to train and improve machine learning models. They can generate a wide variety of diseased potato images, increasing the size and diversity of training datasets, which enables models to better generalize and identify potato diseases. By producing realistic images reflecting natural disease patterns, GANs assist researchers and farmers in developing more accurate diagnostic tools. Moreover, explainable AI is pivotal in building trust with agricultural professionals by offering transparency into the decision-making processes of AI models; providing visual explanations for disease classification fosters confidence among farmers and researchers reliant on these technologies. Instance segmentation in agriculture, particularly for tasks like potato disease detection, involves identifying and delineating individual potato plants or specific infected areas within each plant at the pixel level, allowing for a detailed analysis of disease severity and spread by accurately segmenting diseased regions from healthy parts of the plant.

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