Breast cancer (BC) is one of the most common types of cancer among women with a high mortality rate. Histopathological analysis facilitates the detection and diagnosis of BC but is a highly time-consuming specialised task, dependent on the experience of the pathologists. Hence, there is a dire need for computer-assisted diagnosis (CAD) to relieve the workload on pathologists. Dr Sudhakar Tummala, Assistant Professor, Department of Electronics and Communication Engineering, has conducted breakthrough research on this domain in his paper titled BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers published in the Q1 Journal Mathematics, having an Impact Factor of 2.6.


Breast cancer (BC) is one of the deadly forms of cancer and a major cause of female mortality worldwide. The standard imaging procedures for screening BC involve mammography and ultrasonography. However, these imaging procedures cannot differentiate subtypes of benign and malignant cancers. Therefore, histopathology images could provide better sensitivity toward benign and malignant cancer subtypes. Recently, vision transformers are gaining attention in medical imaging due to their success in various computer vision tasks. Swin transformer (SwinT) is a variant of vision transformer that works on the concept of non-overlapping shifted windows and is a proven method for various vision detection tasks. Hence, in this study, we have investigated the ability of an ensemble of SwinTs for the 2- class classification of benign vs. malignant and 8-class classification of four benign and four malignant subtypes, using an openly available BreaKHis dataset containing 7909 histopathology images acquired at different zoom factors of 40×, 100×, 200× and 400×. The ensemble of SwinTs (including tiny, small, base, and large) demonstrated an average test accuracy of 96.0% for the 8-class and 99.6% for the 2-class classification, outperforming all the previous works. Hence, an ensemble of SwinTs could identify BC subtypes using histopathological images and may lead to pathologist relief.

A brief summary of the research in layperson’s terms

Breast cancer (BC) is the second deadliest cancer after lung cancer, causing morbidity and mortality worldwide in the women population. Its incidence may increase by more than 50% by the year 2030 in the United States. The non-invasive diagnostic procedures for BC involve a physical examination and imaging techniques such as mammography, ultrasonography and magnetic resonance imaging. However, the physical examination may not detect it early, and Imaging procedures offer low sensitivity for a more comprehensive assessment of cancerous regions and identification of cancer subtypes. Histopathological imaging via breast biopsy, even though minimally invasive, may provide accurate identification of the cancer subtype and precise localization of the lesion. However, this manual examination by the pathologist could be tiresome and prone to errors. Therefore, automated methods for BC subtype classification are warranted.

Deep learning has revolutionised many areas in the last decade, including healthcare for various tasks such as accurate disease diagnosis, prognosis, and robotic-assisted surgery. There were studies based on deep convolutional neural networks (CNN) for detecting BC using the aforementioned imaging procedures. However, CNNs exhibit inherent inductive bias and are variant to translation, rotation, and location of the object of interest in the image. Therefore, image augmentation is generally applied while training CNN models, although the data augmentation may not provide expected variations in the training set. Hence, self-attention based deep learning models that are more robust towards the orientation and location of an object of interest in the image are rapidly growing.

SwinTs are an improved version of earlier vision transformer (ViT) architecture and are hierarchical vision transformers using shifted windows that work based on self-attention. For efficient modelling, self-attention within local windows was proposed and computed, and to evenly partition the image, the windows are arranged in a non-overlapping manner. The window-based self-attention has linear complexity and is scalable. However, the modelling power of window-based self-attention is limited because it lacks connections across windows. Therefore, a shifted window partitioning approach that alternates between the partitioning configurations in consecutive Swin transformer blocks was proposed to allow cross-window connections while maintaining the efficient computation of non-overlapping windows. The shifted window scheme in Swin transformers offers increased efficiency by restricting self- attention computation to local windows that are non-overlapping while also facilitating a cross-window connection. Overall, the SwinT network’s performance was superior to that of the standard ViTs.

Therefore, the paper analyses the ability of an ensemble of Swin transformer models (BreaST-Net) for the automated multi-class classification of BC by investigating histopathological images. The work dealt with both benign and malignant subtypes. Further, the benign cancer subtypes include fibroadenoma, tubular adenoma, phyllodes tumour, and adenosis. Whereas the malignant subtypes contain ductal carcinoma, papillary carcinoma, lobular carcinoma, and mucinous carcinoma.

Social implications of the research

Dr Sudhaker Tummala explains that the computer-aided subtyping of breast cancer from histopathology images using an ensemble of fine-tuned SwinT models can be an alternative to manual diagnoses, thereby reducing the burden on clinical pathologists.


  1. Prof. Seifedine Kadry, Department of Applied Data Science, Noroff University College, Kristiansand, Norway
  2. Dr Jungeun Kim, Division of Computer Science, Department of Software, Kongju National University, Korea

In the future, Dr Tummala will advance his research to add explainability to the ensemble model predictions and also to develop models that can work on fewer data samples.

“Team NAVAN” – a multi-disciplinary technical team, comprising of undergraduate students from various engineering branches such as Mechanical, EEE, Software and Information Technology participated in the technical competition APOGEE 2017, at BITS Pilani. The team is working on a project of a magnetic elevator which is based on the principle of magnetic levitation. The horizontal magnetic levitation is very commonly used but the vertical levitation is the most challenging work and team NAVAN has successfully completed the working model of the elevator which was presented at BITS Pilani and IIT Kanpur (results are awaited).

About 106 teams from reputed institutes including entrepreneurs had registered for the event but only 28 of them reached the final round and team NAVAN was one amongst them. In the finals, the teams were evaluated in different categories such as innovation, feasibility, robustness and working model. Team NAVAN scored high in all the categories and were declared “Winners” in the domain of Prototype project presentation under Design Appliances Category at APOGEE 2017.

The team’s motto is to work for the society as they believe that Engineering is to serve the society. Their upcoming works will focus on the “Society Need Products” and they have aimed to make products which will reach the market. The team is mentored by Mr.Shubrajit Bhaumik and Mr.Sodi Setty Prasad – Assistant Professors from the Department of Mechanical Engineering.

The Honourable Chief Minister of Andhra Pradesh Mr. N Chandrababu Naidu unveiled the master plan for the new and fast upcoming SRM University, Andhra Pradesh, which is set to commence programs in engineering from August of this year.

“SRM is embracing a new method of learning, not seen before in India. We will emphasize practical and application oriented learning via projects and lab courses rather than monologues that are the typical feature of our classrooms. We would like to create a world class environment for our students here at SRM University, Andhra Pradesh; and we want to help them create tomorrow”, says Dr. P Sathyanarayanan, President of SRM University, Andhra Pradesh.

To help them in this endeavour SRM University, Andhra Pradesh has brought in many multinational institutions. Perkins + Will, a reputed international architecture firm with many decades of experience is conceptualizing the design of the university. This will be constructed by the eminent Shapoorji Pallonji Group. Aiding them in recruitment of senior administrative and academic members are international search firms Perret Laver and Society, who specialize in the higher education space.

SRM University, Andhra Pradesh is currently establishing partnerships with reputed international universities from the US, the UK and Singapore. With support from these international institutions, there will be a significant focus on both pure and applied research in areas of renewable energy, space technology, IoT, blue economy, nanotechnologies, and many more.“Developing research led universities is fundamental to the progress of the state and the nation. We aim to do just that by bringing solutions to the problems that plague today’s world”, says Dr. Narayana Rao, Pro – Vice Chancellor of SRM University, Andhra Pradesh.

This university is aimed to become first integrated inter disciplinary institutions offering courses in Engineering, Management, law, medical sciences, pure sciences and liberal arts.

Additionally, SRM University, Andhra Pradesh, along with the Chief Minister of AP, is launching an incubation centre “SHRISTI” – SRM Habitat for Research & Innovation in Science & Technology for Industries to make its university a hub of innovation of development.

SHRISTI aims to create 100 companies and generate 10,000 jobs in next 10 years.  This innovation centre will provide customised build-up space and research infrastructure with supporting services.

October 5-11, 2017

Massachusetts Institute of Technology, Cambridge, USA

As part of SRM University, AP – Amaravati’s engagement with Massachusetts Institute of Technology (MIT), USA, a design camp for SRM’s faculty was conducted to help prepare them to teach MIT’s courses. The design camp, conducted in MIT, emphasized on active and collaborative learning principles and practice, and gave faculty the necessary exposure to build on interactive, hands-on, technology-enabled experiences, and blended/hybrid delivery modes that combine online and face-to-face learning

The participants of the design camp were provided guidance on integration of MITx courses and OpenCourseWare material within relevant local contexts. The design camp also trained them on identifying learning outcomes, and alignment of curriculum design, course content, learning activities, and assessment with selected degree objectives of SRM University, Andhra Pradesh.