Histopathology image segmentation requires not only accurate pixel-wise predictions but also the preservation of topological structures and the quantification of prediction uncertainty for better diagnostics. We present HISTO-UNet, a novel framework that addresses these challenges by integrating topology-preserving constraints with a dual uncertainty quantification system for robust histopathology image segmentation. Our approach employs a multi-task objective, combining medial axis and marker-controlled topology losses with a Bayesian deep learning methodology to simultaneously capture both aleatoric and epistemic uncertainty. Through extensive evaluation on three public datasets, we found HISTO-UNet consistently outperforms standard baselines in both segmentation accuracy and uncertainty calibration. Our experiments validate the contribution of each component, revealing that the integration of topology-awareness and dual uncertainty quantification yields significant improvements in both segmentation performance and model reliability.

Retrieval-Augmented Generation (RAG) systems are susceptible to factual inconsistencies when retrieved evidence is conflicting, a common issue with open-domain sources. Prevailing multi-agent approaches attempt to resolve this through unstructured debates that treat all information sources as equally credible. Concurrently, reliability-aware systems address source quality but typically only as a weighting factor during final aggregation, failing to integrate this crucial signal into the reasoning process itself. This paper proposes DARE (A Dialectical Adversarial RAG Engine), a novel framework that implements a structured dialectical process to resolve such conflicts through an evidence-aware adversarial agent that initiates a structured cross-examination of claims made by other agents. This process forces each claim to be defended against the complete set of source documents, allowing the system to dynamically infer an argument’s credibility based on its logical resilience. By organizing the debate as a structured dialectic, DARE provides a more robust and systematic framework for synthesizing truth from unreliable and conflicting information. Empirical experiments across three challenging benchmarks show that DARE consistently outperforms strong baselines, achieving performance gains of up to 77

Brain hemorrhage, or Intracranial Hemorrhage (ICH), is a critical medical condition requiring rapid diagnosis. Existing Convolutional Neural Network (CNN) models often struggle to differentiate similar hemorrhage subtypes like Epidural (EDH) and Subdural (SDH) due to a lack of specific spatial feature identification. This study aims to develop a robust classification framework to address this challenge. We propose an ensemble framework combining two complementary models. The first is an attention-gated 2D CNN designed to highlight subtle hemorrhagic regions. The second is a multi-level Discrete Wavelet Transform (DWT) model that analyzes images in the frequency domain to capture deeper contextual and textural information from the 3D brain volume. The proposed ensemble model was evaluated on the RSNA, CQ500, and a new GMC clinical dataset. The empirical study demonstrates that our model consistently outperforms state-of-the-art methods across standard evaluation metrics, including accuracy, macro-averaged AUC-ROC, specificity, sensitivity, and F1-score. The novel ensembling of an attention-gated CNN and a DWT-based model provides a more comprehensive feature representation, leading to significantly improved accuracy and robustness in ICH classification, particularly in distinguishing challenging subtypes like EDH and SDH.

Accurate identification and labeling of biomedical entities, such as diseases, genes, chemical and species, within scientific texts are crucial for understanding complex relationships. We propose Adaptive BERT or AdaBioBERT, a robust named entity recognition (NER) model that builds upon BioBERT (Biomedical Bidirectional Encoded Representation from Transformers) based on an adaptive loss function to learn different types of biomedical token sequence. This adaptive loss function combines the standard Cross Entropy (CE) loss and Conditional Random Field (CRF) loss to optimize both token level accuracy and sequence-level coherence. AdaBioBERT captures rich semantic nuances by leveraging pre-trained contextual embeddings from BioBERT. On the other hand, the CRF loss of AdaBioBERT ensures proper identification of complex multi-token biomedical entities in a sequence and the CE loss can capture the simple unigram entities in a sequence. The empirical analysis on multiple standard biomedical coprora demonstrates that AdaBioBERT performs better than the state of the arts for most of the datasets in terms of macro and micro averaged Fl score.

Convolutional Neural Networks (CNNs) have achieved significant success in classifying radiology images; however, their implementation often resembles a "black box," limiting medical practitioners' ability to comprehend and trust the decisions made due to a lack of interpretability. Recent advancements in patch-based prototypical networks have sought to enhance the interpretability of image classification systems. Still, the use of these models, specifically developed for the radiology domain, has been limited. This paper presents ProtoRadNet - Prototypical Patches of Convolutional Features for Radiology Image Classification Network. ProtoRadNet provides explicit visualisations of the prototypes identified within an image during classification tasks, thereby offering transparent reasoning for its decisions and effectively bridging the divide between CNN findings and their practical implications to the domain experts. The primary objective of ProtoRadNet is to identify significant prototypes of convolutional features within individual classes and across all classes, refining the CNN's training to bolster interpretability rather than relying on all convolutional features indiscriminately. The model achieves localised and global interpretability by integrating inter-class and intra-class prototypes, enhancing overall decision-making processes. This interpretability is particularly noteworthy as it is accomplished using only image-level ground truths, rendering it semantically meaningful for real-world applications, where detailed annotations are frequently unavailable or time-consuming. Empirical evaluation demonstrates that ProtoRadNet surpasses state-of-the-art in most cases. It achieves macro-averaged F1-scores of 92.16%,96.14% and 29.32% with an improvement of +2.04%,+0.73% and +0.41% respectively than the best competing method on Brain MRI, Chest CT and MIMIC CXR-LT datasets. These results show the value and validity of our ProtoRadNet model.

Mallampati classification is an indication to predict whether a patient might have crowded airways. According to the scale, there are four classes with increasing severity of airway crowding, which may indicate obstructive sleep apnea, as reported in multiple studies. Conventionally, the Mallampati scale is manually identified by an expert in the clinic, but the same can be done by assessing the image of a person's oral cavity. In this regard, this study aims to develop a deep learning framework to perform Mallampati classification using the oral cavity images of individuals. The proposed framework for Mallampati classification develops a loss function by combining the aleatoric and epistemic uncertainty principles to improve the reliability of predictions of the ConvNeXt model for image classification. The experimental analysis was performed on a dataset of 262 subjects acquired from the sleep lab at All India Institute of Medical Sciences Bhopal in India, demonstrating that the proposed framework performs better than the state-of-the-art in terms of common evaluation metrics. Notably, experimental results reveal that the proposed uncertainty principle performs well as the experiments were conducted on all states of the arts with and without using this principle.

Brain cancer ranks among the top ten causes of death globally and stands as the second leading cause of cancer-related deaths among adolescents. Magnetic Resonance Imaging (MRI) is widely used as one of the most important diagnostic imaging techniques for early detection of tumors in brain cancer. In general, the contour is manually identified by experts from the MRIs, causing errors due to subjective assessments. In order to address this issue, image segmentation techniques can be used to automate the process of identifying and delineating the tumor region from the MR images. In this work, we propose a novel deep-learning framework named TCPNet, which is developed in the spirit of the U-Net model. The proposed architecture ensures that the model segments the tumor contours and explicitly estimates data and model uncertainties in the predictions, which is essential for tumor contour detection. Our extensive study on two open-source brain MRI datasets shows that TCPNet performs better than U-Net and other state-of-the-arts in terms of common evaluation metrics. Additionally, the proposed model presents uncertainties in model predictions, demonstrating confidence in segmented tissues or advising for expert intervention if necessary.

This paper develops a novel regression framework to estimate international tourist arrivals in 37 countries from the Organization for Economic Co-operation and Development (OECD) countries by combining significant socio-economic-environment features and a natural language processing (NLP) based social media index. The index is developed by fine-tuning a pre-trained BERT model using the reviews of different countries collected from TripAdvisor to generate a tourist feedback score, which is used as an additional feature with the other OECD features for tourism arrival estimation using an adaptive boosting regression technique. The outcomes demonstrate that the proposed framework performs reasonably well than other relevant regression techniques. The findings of this study can be utilized to make decisions that support the growth of sustainable tourism.

The Forum for Information Retrieval (FIRE) started a shared task this year for classification of comments of different code segments. This is binary text classification task where the objective is to identify whether comments given for certain code segments are relevant or not. The BioNLP-IISERB group at the Indian Institute of Science Education and Research Bhopal (IISERB) participated in this task and submitted five runs for five different models. The paper presents the overview of the models and other significant findings on the training corpus. The methods involve different feature engineering schemes and text classification techniques. The performance of the classical bag of words model and transformer-based models were explored to identify significant features from the given training corpus. We have explored different classifiers viz., random forest, support vector machine and logistic regression using the bag of words model. Furthermore, the pre-trained transformer based models like BERT, RoBERT and ALBERT were also used by fine-tuning them on the given training corpus. The performance of different such models over the training corpus were reported and the best five models were implemented on the given test corpus. The empirical results show that the bag of words model outperforms the transformer based models, however, the performance of our runs are not reasonably well in both training and test corpus. This paper also addresses the limitations of the models and scope for further improvement.

The eRisk lab at CLEF 2022 had released three different tasks based on the posts of different users over Reddit, a popular social media. The first task was early detection of signs of pathological gambling. The second task was the early prediction of depression. The third one was assessing the severity of eating disorders over social media posts. The BioNLP research group at the Indian Institute of Science Education and Research Bhopal (IISERB) participated in all three tasks and submitted five runs using five different text mining frameworks for task 1 and task 2 and four different runs for task 3. The methods involve different feature engineering schemes and text classification techniques. The performance of the classical bag of words model, paragraph embedding technique and transformer-based models were explored to identify significant features from the given corpora. Moreover, we have identified features based on the biomedical concepts for pathological gambling using Unified Medical Language Systems, a repository for biomedical vocabularies. Subsequently, we have explored the performance of different classifiers, e.g., logistic regression, random forest etc. using various such features generated from the given data. The official results on the test data of individual tasks show that the proposed frameworks achieve top scores in terms of some of the evaluation techniques, e.g., precision, F1 score, speed etc. for all three tasks. The paper describes the performance, value and validity of the proposed frameworks for individual tasks and the scopes for further improvement.

The DepSign-LT-EDI-ACL2022 shared task focuses on early prediction of severity of depression over social media posts. The BioNLP group at Department of Data Science and Engineering in Indian Institute of Science Education and Research Bhopal (IISERB) has participated in this challenge and submitted three runs based on three different text mining models. The severity of depression were categorized into three classes, viz., no depression, moderate, and severe and the data to build models were released as part of this shared task. The objective of this work is to identify relevant features from the given social media texts for effective text classification. As part of our investigation, we explored features derived from text data using document embeddings technique and simple bag of words model following different weighting schemes. Subsequently, adaptive boosting, logistic regression, random forest and support vector machine (SVM) classifiers were used to identify the scale of depression from the given texts. The experimental analysis on the given validation data show that the SVM classifier using the bag of words model following term frequency and inverse document frequency weighting scheme outperforms the other models for identifying depression. However, this framework could not achieve a place among the top ten runs of the shared task. This paper describes the potential of the proposed framework as well as the possible reasons behind mediocre performance on the given data.

The task of text clustering is to partition a set of text documents into different meaningful groups such that the documents in a particular cluster are more similar to each other than the documents of other clusters according to a similarity or dissimilarity measure. Therefore, the role of similarity measure is crucial for producing good-quality clusters. The content similarity between two documents is generally used to form individual clusters, and it is measured by considering shared terms between the documents. However, the same may not be effective for a reasonably large and high-dimensional corpus. Therefore, a similarity measure is proposed here to improve the performance of text clustering using spectral method. The proposed similarity measure between two documents assigns a score based on their content similarity and their individual similarity with the shared neighbours over the corpus. The effectiveness of the proposed document similarity measure has been tested for clustering of different standard corpora using spectral clustering method. The empirical results using some well-known text collections have shown that the proposed method performs better than the state-of-the-art text clustering techniques in terms of normalized mutual information, f-measure and v-measure.

CLEF SimpleText 2022 lab focuses on developing effective systems to identify relevant passages from a given set of scientific articles. The lab has organized three tasks this year. Task 1 is focused on passage retrieval from the given data for a query text. These passages can be complex and hence require further simplification to be carried out in tasks 2 and 3. The BioNLP research group at the Indian Institute of Science Education and Research Bhopal (IISERB) in collaboration with two different information retrieval research groups at IISER Kolkata and ISI Kolkata participated only in Task 1 of this challenge and submitted three runs using three different retrieval models. The paper explores the performance of these retrieval models for the given task. We used a standard BM25 model as our first run to identify 1000 relevant passages for each query. Moreover, the passages for each query were ranked based on their similarity scores generated by the BM25 model. For our second run, we used a BERT (Bidirectional Encoder Representations from Transformers) based re-ranking method, called as Mono-BERT to further rank the 1000 passages retrieved by our first run for each query. A pre-trained sequence to sequence model based re-ranking method, called MonoT5 was used as our third run to reorder the 1000 passages retrieved by the Mono-BERT model for each query. As the official results of this task are not yet announced, we cannot explore the performance of our submissions. However, we have manually checked the retrieved results of many queries for each run, which indicate that the performance improved from run 1 to run 2 and further to run 3.

The Task 2 of CLEF eRisk 2021 challenge focuses on early prediction of self-harm based on sequentially processing pieces of text over social media. The workshop has organized three tasks this year and released different corpora for the individual tasks and these are developed using the posts and comments over Reddit, a popular social media. The text mining group at Center for Computational Biology in University of Birmingham, UK has participated in Task 2 of this challenge and submitted five runs for five different text mining frameworks. The paper explore the performance of different text mining techniques for early risk prediction of self-harm. The techniques involve various classifiers and feature engineering schemes. The simple bag of words model and the Doc2Vec based document embeddings have been used to build features from free text. Subsequently, ada boost, random forest, logistic regression and support vector machine (SVM) classifiers are used to identify self-harm from the given texts. The experimental analysis on the test corpus show that the SVM classifier using the conventional bag of words model outperforms the other methods for identifying self-harm. This framework achieves best score in terms of precision among all the submissions of eRisk 2021 challenge for identifying self harm over social media.

The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.

Automated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the task of grouping similar objects and segregating dissimilar objects. The community detection problem can be thought of as finding groups of densely interconnected nodes with few connections to nodes outside the group. A node similarity measure is proposed here that finds the similarity between two nodes by considering both neighbors and non-neighbors of these two nodes. Subsequently, a method is introduced for identifying communities in complex networks using this node similarity measure and the notion of data clustering. The significant characteristic of the proposed method is that it does not need any prior knowledge about the actual communities of a network. Extensive experiments on several real world and artificial networks with known ground-truth communities are reported. The proposed method is compared with various state of the art community detection algorithms by using several criteria, viz. normalized mutual information, f-measure etc. Moreover, it has been successfully applied in improving the effectiveness of a recommender system which is rapidly becoming a crucial tool in e-commerce applications. The empirical results suggest that the proposed technique has the potential to improve the performance of a recommender system and hence it may be useful for other e-commerce applications.