8 citations · 29 across the 18 of their papers we have counts for
19 papers
Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging
Saroj Khanal, Yashawant Kumar Yadav, Kritam Bhattarai +12
Diffusion-weighted imaging (DWI) is widely used in clinical settings but remains vulnerable to geometric distortion. Conventional correction methods often require additional acquis…
Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset
Prabin Bohara, Pralhad Kumar Shrestha, Arpan Rai +8
Accurate automatic brain tumor segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging protocols, diverse imaging data, ex…
Agentic Markets: Equilibrium Effects of Improving Consumer Search
Brendan Lucier, Nicole Immorlica, Markus Mobius +5
Motivated by agentic markets -- two-sided markets in which consumers and businesses are assisted by AI tools that facilitate consumers' search -- we study the impact of improved se…
Evaluating Large Language Models' Responses to Sexual and Reproductive Health Queries in Nepali
Medha Sharma, Supriya Khadka, Udit Chandra Aryal +15
As Large Language Models (LLMs) become integrated into daily life, they are increasingly used for personal queries, including Sexual and Reproductive Health (SRH), allowing users t…
The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI
Alok Khatri, Bishesh Khanal
Artificial intelligence (AI) represents a qualitative shift in technological change by extending cognitive labor itself rather than merely automating routine tasks. Recent evidence…
From Development to Deployment of AI-assisted Telehealth and Screening for Vision- and Hearing-threatening diseases in resource-constrained settings: Field Observations, Challenges and Way Forward
Mahesh Shakya, Bijay Adhikari, Nirsara Shrestha +7
Vision- and hearing-threatening diseases cause preventable disability, especially in resource-constrained settings(RCS) with few specialists and limited screening setup. Large scal…
Multimodal Latent Fusion of ECG Leads for Early Assessment of Pulmonary Hypertension
Mohammod N. I. Suvon, Shuo Zhou, Prasun C. Tripathi +7
Recent advancements in early assessment of pulmonary hypertension (PH) primarily focus on applying machine learning methods to centralized diagnostic modalities, such as 12-lead el…
Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset
Bijay Adhikari, Pratibha Kulung, Jakesh Bohaju +6
Automating brain tumor segmentation using deep learning methods is an ongoing challenge in medical imaging. Multiple lingering issues exist including domain-shift and applications…
NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech Detection using Ensembling of BERT-based models
Anmol Guragain, Nadika Poudel, Rajesh Piryani +1
This paper explores hate speech detection in Devanagari-scripted languages, focusing on Hindi and Nepali, for Subtask B of the CHIPSAL@COLING 2025 Shared Task. Using a range of tra…
TuneVLSeg: Prompt Tuning Benchmark for Vision-Language Segmentation Models
Rabin Adhikari, Safal Thapaliya, Manish Dhakal +1
Vision-Language Models (VLMs) have shown impressive performance in vision tasks, but adapting them to new domains often requires expensive fine-tuning. Prompt tuning techniques, in…
VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks
Manish Dhakal, Rabin Adhikari, Safal Thapaliya +1
Foundation Vision-Language Models (VLMs) trained using large-scale open-domain images and text pairs have recently been adapted to develop Vision-Language Segmentation Models (VLSM…
AI-Assisted Cervical Cancer Screening
Kanchan Poudel, Lisasha Poudel, Prabin Raj Shakya +3
Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical cancer screening test in resource-constrained settings of low- and middle-income countries (LMICs), whic…
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?
Bidur Khanal, Binod Bhattarai, Bishesh Khanal +1
Noisy labels can significantly impact medical image classification, particularly in deep learning, by corrupting learned features. Self-supervised pretraining, which doesn't rely o…
Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in Echocardiography
Rabin Adhikari, Manish Dhakal, Safal Thapaliya +3
Accurate segmentation is essential for echocardiography-based assessment of cardiovascular diseases (CVDs). However, the variability among sonographers and the inherent challenges…
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining
Bidur Khanal, Binod Bhattarai, Bishesh Khanal +1
Noisy labels hurt deep learning-based supervised image classification performance as the models may overfit the noise and learn corrupted feature extractors. For natural image clas…
Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models
Kanchan Poudel, Manish Dhakal, Prasiddha Bhandari +3
Medical image segmentation allows quantifying target structure size and shape, aiding in disease diagnosis, prognosis, surgery planning, and comprehension.Building upon recent adva…
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
Bidur Khanal, Binod Bhattarai, Bishesh Khanal +2
Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize…
Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images
Suprim Nakarmi, Sanam Pudasaini, Safal Thapaliya +5
The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, a…
COVID-19-related Nepali Tweets Classification in a Low Resource Setting
Rabin Adhikari, Safal Thapaliya, Nirajan Basnet +3
Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemi…